AI Platform Engineer Jobs in Massachusetts
AI Platform Engineer jobs in Massachusetts are among the most active in the country, concentrated in life sciences technology, financial services, and enterprise software, with openings from junior MLOps roles through principal-level platform architects. The deepest hiring activity is in Greater Boston, Cambridge, and the Route 128 corridor, where employers like MathWorks, Biogen, and Fidelity Investments maintain large engineering organizations that depend on scalable AI infrastructure. The most in-demand specialties are LLM deployment pipelines, cloud-native ML platform development, and AI observability tooling. Find a role that fits below and apply directly.
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Job Summary
Senior Director, Enterprise AI Platform EngineeringThe Senior Director, Enterprise AI Platform Engineering will define and lead Insulet’s enterprise AI platform vision, strategy, and architecture, enabling the scalable adoption of Microsoft Copilot, custom copilots, AI agents, intelligent automation, and advanced AI-driven services across the organization. This leader will own end-to-end accountability for AI platform engineering, operations, governance, architecture, observability, and AI service delivery, driving enterprise standards, risk management, and technology modernization.
The ideal candidate is a strategic enterprise leader who can influence at the executive level while building and leading high-performing, multidisciplinary teams that translate AI innovation into measurable business value.


Senior Director, Enterprise AI Platform Engineering
The Senior Director, Enterprise AI Platform Engineering will set the enterprise vision, strategy, architecture, and operating model for Insulet's AI platform ecosystem. This role will own the capabilities required to safely scale Microsoft Copilot, custom copilots, AI agents, knowledge retrieval, document intelligence, semantic intelligence, and AI-powered workflow automation across the enterprise.
As a senior leader within the Enterprise AI and Data organization, this role will have end-to-end accountability across AI platform engineering, AI operations, governance automation, enterprise AI architecture, enablement, FinOps, observability, and reusable AI services. The role will shape long-term platform investments, enterprise standards, risk controls, and technology modernization priorities.
The ideal candidate is an enterprise platform leader who can operate at CTO and ELT levels while building and leading a multi-layer, diverse organization of directors, senior managers, architects, engineers, product leaders, and technical specialists.
Key Responsibilities
1. Enterprise AI Platform Strategy, Architecture, and Investment
- Define and own a 5+ year enterprise AI platform vision, architecture strategy, capability roadmap, operating model, and investment plan aligned with company strategy and technology modernization priorities.
- Set the enterprise roadmap across Microsoft Copilot, Copilot Studio, Azure AI, large language models, RAG, agents, document intelligence, semantic search, vector stores, orchestration frameworks, model gateways, and reusable AI services.
- Establish enterprise decision frameworks, reference architectures, platform patterns, reusable blueprints, and engineering standards that balance speed, security, compliance, interoperability, performance, reliability, and cost.
- Present platform strategy, investment recommendations, build-versus-buy decisions, vendor choices, value cases, and risk assessments to the CTO, ELT, and senior business and technology leaders.
- Partner with enterprise executives and technology leaders to align AI platform investments with business priorities, risk expectations, and broader modernization roadmaps.
2. AI Operations, Copilot, Agentic AI, and Enterprise Enablement
- Own the enterprise capabilities and operating model for AI operations, LLMOps, platform reliability, AI enablement, developer experience, product onboarding, and production support.
- Lead engineering patterns and reusable assets for Microsoft 365 Copilot, Copilot Studio, Teams-based assistants, custom copilots, role-based business assistants, and enterprise AI agents embedded into workflows.
- Establish enterprise enablement services, including onboarding, reference implementations, prompt and agent libraries, connectors, integration adapters, evaluation harnesses, playbooks, and communities of practice.
- Drive adoption across Commercial, Customer Service, Finance, Supply Chain, Product, Quality, Regulatory, R&D, and enterprise functions while reducing fragmented or duplicative solutions.
3. Knowledge, Document, and Semantic Intelligence Platforms
- Own enterprise knowledge retrieval, document intelligence, and semantic architecture, including ingestion, metadata, access-aware retrieval, vector indexing, source attribution, citation quality, and lifecycle standards.
- Scale reusable document intelligence capabilities for classification, OCR, extraction, summarization, search, automation, and unstructured data processing.
- Establish semantic intelligence capabilities, including business glossaries, ontologies, semantic models, metadata catalogs, knowledge graphs, domain context layers, and reusable definitions.
- Define enterprise data-readiness standards for AI, including authoritative sources, permissions, lineage, freshness, retention, quality thresholds, and human validation.
4. Governance Automation, Responsible AI Operations, FinOps, and Risk
- Own governance automation and policy-as-code capabilities that embed responsible AI, privacy, security, quality, and compliance controls into the platform lifecycle.
- Establish enterprise AI FinOps, including consumption visibility, cost allocation, forecasting, capacity planning, model routing, caching, and cost-to-value reporting.
- Set LLMOps and AI observability standards for model and prompt performance, retrieval quality, hallucination risk, citation accuracy, latency, usage, incidents, user feedback, and production reliability.
- Partner with Security, Privacy, Legal, Compliance, Finance, Quality, Regulatory, and Data Governance to provide the CTO and ELT with platform risk assessments, control effectiveness, and remediation priorities.
5. Engineering Excellence and Enterprise Delivery
- Own a portfolio of reusable AI platform services, APIs, connectors, prompt modules, agent frameworks, model gateways, evaluation tools, monitoring capabilities, and semantic services.
- Set enterprise engineering standards for versioning, testing, CI/CD, deployment, monitoring, documentation, incident response, lifecycle management, resilience, and retirement.
- Establish portfolio governance, funding priorities, delivery mechanisms, service levels, adoption measures, and value realization across platform capabilities.
- Drive enterprise adoption of common platforms and reusable services, reducing duplicate builds, fragmentation, technical debt, and time from experimentation to trusted production.
6. Multi-Layer Organizational Leadership and Enterprise Influence
- Build, lead, and develop a multi-layer, diverse organization of directors, senior managers, architects, engineers, product leaders, and technical specialists accountable for enterprise AI platform outcomes.
- Define the platform organization design, talent strategy, workforce plan, leadership structure, and succession pipeline required to scale enterprise capabilities.
- Serve as the enterprise thought leader for AI platform engineering and influence technology strategy, architecture, investments, vendor decisions, risk posture, and modernization priorities at CTO and ELT levels.
- Provide formal leadership through direct management and enterprise leadership through influence across Technology, Cybersecurity, Data and Analytics, Product Development, R&D, Quality, Regulatory, Commercial, Digital, and Operations.
Required Qualifications
- Bachelor's or Master's degree in computer science, engineering, data science, information systems, analytics, or a related technical field.
- 18+ years of progressive experience leading enterprise-scale technology, data, analytics, AI, platform engineering, or cloud engineering organizations, including significant leadership of leaders and multi-disciplinary teams.
- Demonstrated experience setting multi-year enterprise platform strategy, owning complex investment portfolios, and delivering secure, reliable, reusable services at scale.
- Strong understanding of generative AI, large language models, RAG, AI agents, copilots, embeddings, vector databases, semantic search, document intelligence, knowledge graphs, MLOps/LLMOps, APIs, and cloud-native engineering.
- Demonstrated ability to influence executive stakeholders and communicate architecture choices, investments, value, and risk at CTO and ELT levels.
Preferred Qualifications
- Experience in healthcare, medical devices, life sciences, diabetes care, digital health, or other regulated industries.
- Hands-on experience with Microsoft 365 Copilot, Copilot Studio, Azure AI, Azure OpenAI, Databricks, Snowflake, Salesforce, ServiceNow, and enterprise integration ecosystems.
- Experience building or operating AI FinOps, governance automation, AI observability, model gateways, prompt management, evaluation frameworks, or enterprise guardrail services.
- Strong executive communication and influence skills, with the ability to translate complex platform choices into clear investments, operating models, and outcomes
.
Success Measures
- Enterprise adoption and reuse of common AI platforms, agents, knowledge retrieval, document intelligence, semantic services, and guardrails.
- Progress against the 5+ year platform roadmap, investment priorities, and strategic capability milestones.
- Reduction in duplicate AI builds, technology fragmentation, technical debt, and time to trusted production.
- Improved cost transparency, platform reliability, retrieval quality, citation accuracy, user trust, and responsible AI control effectiveness.
- Strength, engagement, and succession depth of the AI platform leadership organization.
NOTE: This position is eligible for hybrid working arrangements (requires on-site work from an Insulet office). #LI-Hybrid
Additional Information:


Compensation & Benefits:

For U.S.-based positions only, the annual base salary range for this role is $280,600.00 - $420,850.00

This position may also be eligible for incentive compensation.

We offer a comprehensive benefits package, including:
• Medical, dental, and vision insurance
• 401(k) with company match
• Paid time off (PTO)
• And additional employee wellness programs

Application Details:
This job posting will remain open until the position is filled.
To apply, please visit the Insulet Careers site and submit your application online.

Actual pay depends on skills, experience, and education.Insulet Corporation (NASDAQ: PODD), headquartered in Massachusetts, is an innovative medical device company dedicated to simplifying life for people with diabetes and other conditions through its Omnipod product platform. The Omnipod Insulin Management System provides a unique alternative to traditional insulin delivery methods. With its simple, wearable design, the tubeless disposable Pod provides up to three days of non-stop insulin delivery, without the need to see or handle a needle. Insulet’s flagship innovation, the Omnipod 5 Automated Insulin Delivery System, integrates with a continuous glucose monitor to manage blood sugar with no multiple daily injections, zero fingersticks, and can be controlled by a compatible personal smartphone in the U.S. or by the Omnipod 5 Controller. Insulet also leverages the unique design of its Pod by tailoring its Omnipod technology platform for the delivery of non-insulin subcutaneous drugs across other therapeutic areas. For more information, please visit insulet.com and omnipod.com.
We are looking for highly motivated, performance-driven individuals to be a part of our expanding team. We do this by hiring amazing people guided by shared values who exceed customer expectations. Our continued success depends on it!
At Insulet Corporation all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.
( Know Your Rights )
Job Summary
Senior Director, Enterprise AI Platform EngineeringThe Senior Director, Enterprise AI Platform Engineering will define and lead Insulet’s enterprise AI platform vision, strategy, and architecture, enabling the scalable adoption of Microsoft Copilot, custom copilots, AI agents, intelligent automation, and advanced AI-driven services across the organization. This leader will own end-to-end accountability for AI platform engineering, operations, governance, architecture, observability, and AI service delivery, driving enterprise standards, risk management, and technology modernization.
The ideal candidate is a strategic enterprise leader who can influence at the executive level while building and leading high-performing, multidisciplinary teams that translate AI innovation into measurable business value.


Senior Director, Enterprise AI Platform Engineering
The Senior Director, Enterprise AI Platform Engineering will set the enterprise vision, strategy, architecture, and operating model for Insulet's AI platform ecosystem. This role will own the capabilities required to safely scale Microsoft Copilot, custom copilots, AI agents, knowledge retrieval, document intelligence, semantic intelligence, and AI-powered workflow automation across the enterprise.
As a senior leader within the Enterprise AI and Data organization, this role will have end-to-end accountability across AI platform engineering, AI operations, governance automation, enterprise AI architecture, enablement, FinOps, observability, and reusable AI services. The role will shape long-term platform investments, enterprise standards, risk controls, and technology modernization priorities.
The ideal candidate is an enterprise platform leader who can operate at CTO and ELT levels while building and leading a multi-layer, diverse organization of directors, senior managers, architects, engineers, product leaders, and technical specialists.
Key Responsibilities
1. Enterprise AI Platform Strategy, Architecture, and Investment
- Define and own a 5+ year enterprise AI platform vision, architecture strategy, capability roadmap, operating model, and investment plan aligned with company strategy and technology modernization priorities.
- Set the enterprise roadmap across Microsoft Copilot, Copilot Studio, Azure AI, large language models, RAG, agents, document intelligence, semantic search, vector stores, orchestration frameworks, model gateways, and reusable AI services.
- Establish enterprise decision frameworks, reference architectures, platform patterns, reusable blueprints, and engineering standards that balance speed, security, compliance, interoperability, performance, reliability, and cost.
- Present platform strategy, investment recommendations, build-versus-buy decisions, vendor choices, value cases, and risk assessments to the CTO, ELT, and senior business and technology leaders.
- Partner with enterprise executives and technology leaders to align AI platform investments with business priorities, risk expectations, and broader modernization roadmaps.
2. AI Operations, Copilot, Agentic AI, and Enterprise Enablement
- Own the enterprise capabilities and operating model for AI operations, LLMOps, platform reliability, AI enablement, developer experience, product onboarding, and production support.
- Lead engineering patterns and reusable assets for Microsoft 365 Copilot, Copilot Studio, Teams-based assistants, custom copilots, role-based business assistants, and enterprise AI agents embedded into workflows.
- Establish enterprise enablement services, including onboarding, reference implementations, prompt and agent libraries, connectors, integration adapters, evaluation harnesses, playbooks, and communities of practice.
- Drive adoption across Commercial, Customer Service, Finance, Supply Chain, Product, Quality, Regulatory, R&D, and enterprise functions while reducing fragmented or duplicative solutions.
3. Knowledge, Document, and Semantic Intelligence Platforms
- Own enterprise knowledge retrieval, document intelligence, and semantic architecture, including ingestion, metadata, access-aware retrieval, vector indexing, source attribution, citation quality, and lifecycle standards.
- Scale reusable document intelligence capabilities for classification, OCR, extraction, summarization, search, automation, and unstructured data processing.
- Establish semantic intelligence capabilities, including business glossaries, ontologies, semantic models, metadata catalogs, knowledge graphs, domain context layers, and reusable definitions.
- Define enterprise data-readiness standards for AI, including authoritative sources, permissions, lineage, freshness, retention, quality thresholds, and human validation.
4. Governance Automation, Responsible AI Operations, FinOps, and Risk
- Own governance automation and policy-as-code capabilities that embed responsible AI, privacy, security, quality, and compliance controls into the platform lifecycle.
- Establish enterprise AI FinOps, including consumption visibility, cost allocation, forecasting, capacity planning, model routing, caching, and cost-to-value reporting.
- Set LLMOps and AI observability standards for model and prompt performance, retrieval quality, hallucination risk, citation accuracy, latency, usage, incidents, user feedback, and production reliability.
- Partner with Security, Privacy, Legal, Compliance, Finance, Quality, Regulatory, and Data Governance to provide the CTO and ELT with platform risk assessments, control effectiveness, and remediation priorities.
5. Engineering Excellence and Enterprise Delivery
- Own a portfolio of reusable AI platform services, APIs, connectors, prompt modules, agent frameworks, model gateways, evaluation tools, monitoring capabilities, and semantic services.
- Set enterprise engineering standards for versioning, testing, CI/CD, deployment, monitoring, documentation, incident response, lifecycle management, resilience, and retirement.
- Establish portfolio governance, funding priorities, delivery mechanisms, service levels, adoption measures, and value realization across platform capabilities.
- Drive enterprise adoption of common platforms and reusable services, reducing duplicate builds, fragmentation, technical debt, and time from experimentation to trusted production.
6. Multi-Layer Organizational Leadership and Enterprise Influence
- Build, lead, and develop a multi-layer, diverse organization of directors, senior managers, architects, engineers, product leaders, and technical specialists accountable for enterprise AI platform outcomes.
- Define the platform organization design, talent strategy, workforce plan, leadership structure, and succession pipeline required to scale enterprise capabilities.
- Serve as the enterprise thought leader for AI platform engineering and influence technology strategy, architecture, investments, vendor decisions, risk posture, and modernization priorities at CTO and ELT levels.
- Provide formal leadership through direct management and enterprise leadership through influence across Technology, Cybersecurity, Data and Analytics, Product Development, R&D, Quality, Regulatory, Commercial, Digital, and Operations.
Required Qualifications
- Bachelor's or Master's degree in computer science, engineering, data science, information systems, analytics, or a related technical field.
- 18+ years of progressive experience leading enterprise-scale technology, data, analytics, AI, platform engineering, or cloud engineering organizations, including significant leadership of leaders and multi-disciplinary teams.
- Demonstrated experience setting multi-year enterprise platform strategy, owning complex investment portfolios, and delivering secure, reliable, reusable services at scale.
- Strong understanding of generative AI, large language models, RAG, AI agents, copilots, embeddings, vector databases, semantic search, document intelligence, knowledge graphs, MLOps/LLMOps, APIs, and cloud-native engineering.
- Demonstrated ability to influence executive stakeholders and communicate architecture choices, investments, value, and risk at CTO and ELT levels.
Preferred Qualifications
- Experience in healthcare, medical devices, life sciences, diabetes care, digital health, or other regulated industries.
- Hands-on experience with Microsoft 365 Copilot, Copilot Studio, Azure AI, Azure OpenAI, Databricks, Snowflake, Salesforce, ServiceNow, and enterprise integration ecosystems.
- Experience building or operating AI FinOps, governance automation, AI observability, model gateways, prompt management, evaluation frameworks, or enterprise guardrail services.
- Strong executive communication and influence skills, with the ability to translate complex platform choices into clear investments, operating models, and outcomes
.
Success Measures
- Enterprise adoption and reuse of common AI platforms, agents, knowledge retrieval, document intelligence, semantic services, and guardrails.
- Progress against the 5+ year platform roadmap, investment priorities, and strategic capability milestones.
- Reduction in duplicate AI builds, technology fragmentation, technical debt, and time to trusted production.
- Improved cost transparency, platform reliability, retrieval quality, citation accuracy, user trust, and responsible AI control effectiveness.
- Strength, engagement, and succession depth of the AI platform leadership organization.
NOTE: This position is eligible for hybrid working arrangements (requires on-site work from an Insulet office). #LI-Hybrid
Additional Information:


Compensation & Benefits:

For U.S.-based positions only, the annual base salary range for this role is $280,600.00 - $420,850.00

This position may also be eligible for incentive compensation.

We offer a comprehensive benefits package, including:
• Medical, dental, and vision insurance
• 401(k) with company match
• Paid time off (PTO)
• And additional employee wellness programs

Application Details:
This job posting will remain open until the position is filled.
To apply, please visit the Insulet Careers site and submit your application online.

Actual pay depends on skills, experience, and education.Insulet Corporation (NASDAQ: PODD), headquartered in Massachusetts, is an innovative medical device company dedicated to simplifying life for people with diabetes and other conditions through its Omnipod product platform. The Omnipod Insulin Management System provides a unique alternative to traditional insulin delivery methods. With its simple, wearable design, the tubeless disposable Pod provides up to three days of non-stop insulin delivery, without the need to see or handle a needle. Insulet’s flagship innovation, the Omnipod 5 Automated Insulin Delivery System, integrates with a continuous glucose monitor to manage blood sugar with no multiple daily injections, zero fingersticks, and can be controlled by a compatible personal smartphone in the U.S. or by the Omnipod 5 Controller. Insulet also leverages the unique design of its Pod by tailoring its Omnipod technology platform for the delivery of non-insulin subcutaneous drugs across other therapeutic areas. For more information, please visit insulet.com and omnipod.com.
We are looking for highly motivated, performance-driven individuals to be a part of our expanding team. We do this by hiring amazing people guided by shared values who exceed customer expectations. Our continued success depends on it!
At Insulet Corporation all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.
( Know Your Rights )
See All 40 AI Platform Engineer Jobs in Massachusetts
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Find AI Platform Engineer JobsAI Platform Engineer Jobs by City in Massachusetts
Where Massachusetts roles are concentrated, by current openings.
AI Platform Engineer Job Market in Massachusetts
A snapshot from current Massachusetts openings, updated as new roles post.
Who's Hiring
Top Industries Hiring
- Technology & Software
- Biotechnology & Pharmaceuticals
- Consulting & Professional Services
- Insurance
- Law & Legal Services
What Massachusetts Employers Look For
The qualifications that appear most often in AI platform engineer jobs across Massachusetts.
- Bachelor's or master's degree in computer science, engineering, or a related technical field
- Hands-on experience building and operating ML platforms using Kubernetes, Kubeflow, or MLflow
- Proficiency in Python and infrastructure-as-code tools such as Terraform or Pulumi
- Experience deploying and monitoring models on AWS, Azure, or Google Cloud at production scale
- Familiarity with data pipeline orchestration tools including Apache Airflow or Prefect
- Strong collaboration skills working across data science, platform engineering, and product teams
AI Platform Engineer Jobs in Massachusetts: Frequently Asked Questions
How do you become a ai platform engineer in Massachusetts?
There is no state-issued license required to work as an ai platform engineer in Massachusetts. The standard path is a bachelor's degree in computer science, software engineering, or a related field, followed by hands-on experience with ML infrastructure and cloud platforms. Many Massachusetts employers, particularly in the biotech and fintech sectors, also value advanced degrees or industry certifications in cloud architecture. Building a portfolio of deployed ML systems strengthens candidacy considerably.
How much do AI platform engineers make in Massachusetts?
AI platform engineers in Massachusetts earn a median of about $109,590 a year, based on May 2025 Bureau of Labor Statistics wage data, ranging from around $55,740 for the lowest 10% to over $183,460 for the top 10%. Pay rises with experience, specialty, and employer.
Which companies hire ai platform engineers in Massachusetts?
Employers hiring ai platform engineers in Massachusetts right now include LangChain, Whoop, and Adheris, based on current listings on Migrate Mate as of September 2026. Massachusetts's density of biotech, fintech, and enterprise software companies means demand is sustained across both large established organizations and well-funded growth-stage firms headquartered in the Greater Boston area.
Which Massachusetts cities have the most ai platform engineer jobs?
Boston, Cambridge, and Waltham have the most ai platform engineer openings in Massachusetts. Boston and Cambridge anchor the market due to their concentration of biotech companies, financial services firms, and major research universities, while suburban nodes along the Route 128 corridor such as Waltham and Burlington draw significant hiring from enterprise software and defense technology employers operating large engineering campuses there.
Are there remote ai platform engineer jobs in Massachusetts?
Yes, and more than most fields. About 70% of ai platform engineer openings tied to Massachusetts are remote or hybrid as of September 2026, reflecting how naturally the work translates to distributed environments. The portions of the role focused on pipeline development, model deployment automation, and infrastructure configuration are most commonly performed fully remotely, while on-call platform reliability work often requires some on-site presence.
How can I get hired as a ai platform engineer in Massachusetts with little or no experience?
The most realistic entry path is through a junior MLOps or platform engineering associate role, often reached from adjacent positions like data engineer, backend software engineer, or DevOps engineer at Massachusetts employers. Large organizations such as Fidelity Investments and MathWorks have new-graduate hiring programs that open into platform-adjacent infrastructure teams. Building and publicly sharing a portfolio of cloud-based ML deployment projects, and earning an entry-level cloud certification, gives candidates a concrete edge in Massachusetts interviews.
Where can I find and apply to ai platform engineer jobs in Massachusetts?
You can find and apply to ai platform engineer jobs in Massachusetts on Migrate Mate, which lists current Massachusetts openings. Find roles that fit your experience and apply directly from the listing.
See All 40 AI Platform Engineer Jobs in Massachusetts
Find roles in Massachusetts that match your experience and apply in just a few clicks.
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