AI Platform Engineer Jobs
AI Platform Engineer jobs are open across cloud services, financial services, healthcare technology, and enterprise software, from new-grad to principal and staff levels, with specializations in MLOps, LLM infrastructure, and data pipeline architecture. Find a role that fits from the openings below and apply directly.
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INTRODUCTION
Insurance touches people during some of the most challenging moments in their lives. Hi Marley is on a mission to transform how the P&C industry communicates, making those moments faster, easier, and more empathetic for carriers and the customers they serve. We build AI-powered software that keeps everyone in the claims conversation informed and connected. If you believe insurance can combine operational excellence and automation with a human touch, we'd love to meet you.
As we continue to grow, we're looking for a Principal AI Platform Engineer to be a foundational hire on our new AI Operations team. This is the engineer who builds the plumbing of our internal agentic workforce: the AWS hosting layer, the Cognito-backed identity model, the Accountability Registry that says who built what, the Agent Registry that governs who can act, and the Tool Catalog that lets our teams compose new capabilities safely.
You'll partner with the CTO and a UX-focused builder to make Hi Marley an AI-native company from the inside — and you'll do it on a strict AWS stack because our customers are insurance carriers who need real auditability, not a wrapper. If you love AWS Bedrock at the depth where Cognito, IAM, and CDK meet, want to define how an entire company governs agents, and have the communication chops to evangelize a platform internally, this is the role for you. Teamwork and shared enthusiasm are a core part of our culture, which is why this role involves joining us in the Boston office for 2-3 days each week.
What You'll Do:
- Build the AI Operations platform on AWS: Own the hosting, deployment, and infra-as-code layer for every internal agent and tool we run. Bedrock for models, Cognito for auth, CDK or Terraform for everything else. No third-party platforms — we are AWS-only by design.
- Architect the registries and catalogs: Design and build the Accountability Registry (people, ownership, roles), the Agent Registry (identity, permissions, data scope, audit trail), and the Tool & App Catalog (discover, register, approve). These are the spine of agentic governance at Hi Marley.
- Solve the identity-inheritance problem: An agent acts with the inherited permissions of the human who invoked it. You'll design and ship the patterns — Cognito + IAM + scoped tokens — that make this real, deterministic, and auditable.
- Build the developer experience for internal agent builders: Templates, SDK helpers, deploy paths, observability, cost tracking. Make it so that every Hi Marley team can ship their own agent without reinventing security or hosting.
- Partner on security tooling with our Compliance & IT lead: Implement the scans, audits, MCP governance hooks, and DLP signals that the AI Security Ops role will use. You build the rails; they build the gatekeeper.
- Evangelize the platform internally: Lunch-and-learns, internal docs, demo droplets, and "office hours" so other teams know what's available and how to use it. Communication and product instinct are as load-bearing as the AWS depth.
- Use AI in your daily flow: Cursor, Claude Code, agentic coding tools — your default question is "could an agent do this?" before you write the code yourself.
What We're Looking For:
- A genuine curiosity about AI and emerging technologies, paired with the judgment to apply them thoughtfully and responsibly.
- Deep AWS expertise: You can architect a multi-tenant platform on AWS without needing to outsource the hard parts. Bedrock, Cognito, IAM, EventBridge, Lambda, DynamoDB or Postgres, plus IaC (CDK or Terraform). Bonus: AgentCore, SageMaker, Workshop Studio.
- Identity & authorization fluency: You've built systems where authorization is the architecture, not bolted on. OAuth 2.1, scoped tokens, role inheritance, least-privilege design — these are second-nature to you.
- Strong product instincts, not just infra: You think about the developer experience of the people consuming your platform. You can design an SDK or a registry schema that other teams actually want to use.
- AI-forward: You've shipped real LLM-backed systems in production — not just prototypes — and have opinions about token cost, latency, evaluation, observability, and where determinism matters vs. where it doesn't.
- MCP / agentic systems literacy: Familiarity with Model Context Protocol or comparable agent-tool patterns. You don't have to have shipped an MCP server, but you should be able to talk credibly about why MCP exists and what its security boundaries are.
- Excellent communicator: You can present platform work to engineers, executives, and customers, and write docs people actually read. This role only succeeds if the rest of the company knows what's available.
- Architectural integrity over framework hype: You'd rather build the right primitive on AWS than reach for a "magical" higher-level abstraction. You can write Python, TypeScript, or Go fluently — the language matters less than the systems thinking.
Compensation, Benefits & Perks:
At Hi Marley, we are committed to fair and transparent pay practices. The annual base salary for this role is expected to fall within the range of [$152,000–$283,000], depending on experience, skills, qualifications, and location. Offers are determined based on these factors as well as internal peer equity. It's most common for new hires to start near the midpoint of the range, allowing room for growth as employees develop in their role.
In addition to base pay, we offer a comprehensive total rewards package that supports both your wellbeing and professional growth, including:
- Equity grants for all employees
- A 4% matching 401(k) program
- Medical, dental, vision, disability, and life insurance coverage for employees working 30+ hours per week
- Monthly wellness stipend
- Paid parental leave
- A flexible vacation policy - we all work hard and take time when we need it
Who We Are:
At Hi Marley, our culture is built on three core values that every employee embodies:
- Max Courage – We encourage our team, our customers, and their customers to dream big, try new ideas, and maximize impact by measuring risk.
- Be Humble – We lead with appreciation and promote a culture of humility, compassion, and openness to learn from anyone, anywhere.
- Ubuntu ("I am because we are") – We believe true success is much bigger than any single individual or company. By aligning our individual aims behind a shared purpose, we can achieve our fullest potential — together.
Life at Hi Marley:
Life at Hi Marley is shaped by collaboration, learning, and genuine connection. We're proud to foster an environment where people can do their best work, feel supported, and grow alongside a team that celebrates both individuality and shared purpose.
- A fun, lively startup culture that embraces creativity and innovation
- Core values-based leadership that guides our decision-making and daily interactions
- A culture of engagement, diversity, inclusion, and belonging — everyone's voice matters
- Flexible, hybrid work environment that values balance and trust
- Ample opportunities to learn, take on new challenges, and make an impact in a fast-growing organization
- Meaningful work that directly supports our mission to help people and organizations communicate with empathy and clarity
Hi Marley is proud to be an equal employment opportunity employer. We celebrate diversity and do not discriminate based on gender, sexual orientation, gender identity, religion, race, veteran status, disability status, or any other characteristic protected by applicable law. We are committed to building an inclusive work environment representing a variety of backgrounds, perspectives, and skills, where all employees are encouraged to be their authentic selves.
Hi Marley participates in E-Verify and will provide the federal government with your Form I-9 information to confirm that you are authorized to work in the U.S. For more information, please review the documents under "E-Verify Poster" here: https://e-verify.uscis.gov/web/OnlineResources.aspx
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Find AI Platform Engineer JobsAI Platform Engineer Job Market
A snapshot from current openings nationwide, updated as new roles post.
Who's Hiring
- Boston Consulting10

- Apple7

- Adobe6

- Databricks5

- Together AI5

Top Industries Hiring
- Technology & Software124
- Consulting & Professional Services25
- Electronics & Hardware23
- Artificial Intelligence15
- Investment & Asset Management11
What Employers Look For
The qualifications that appear most often in AI platform engineer jobs.
- Proficiency in Python and at least one infrastructure-as-code tool such as Terraform
- Hands-on experience with Kubernetes, Docker, and container orchestration at scale
- Experience building or maintaining MLOps pipelines using tools like Kubeflow, MLflow, or similar
- Familiarity with major cloud platforms including AWS, Google Cloud, or Microsoft Azure
- Bachelor's degree in computer science, engineering, or a related technical field
- Experience with data pipeline frameworks such as Apache Spark, Airflow, or Ray
Tips for Your AI Platform Engineer Job Search
Quantify your model serving infrastructure
Hiring managers for ai platform engineer roles want to see scale, not just tools. Rewrite your resume bullets to show request throughput, latency improvements, or cost reductions you delivered when deploying or maintaining model serving systems.
Distinguish MLOps from platform engineering clearly
Many ai platform engineer postings blur the line between MLOps tooling and core infrastructure work. Read each job description carefully and mirror its language in your application so your background maps to what that specific team actually builds.
Apply early to roles that fit
Migrate Mate lists ai platform engineer openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Build a portfolio around reproducible pipelines
A GitHub repository showing a working feature store, model registry, or CI/CD pipeline for ML models signals practical ability faster than certifications alone. Interviewers for this role consistently probe whether you can design reproducible, observable systems.
Prepare for system design rounds on ML infrastructure
AI platform interviews almost always include a distributed systems or ML infrastructure design round. Practice designing low-latency inference pipelines, batch training orchestration, and data versioning systems out loud so you can communicate tradeoffs clearly under time pressure.
Negotiate scope before you negotiate salary
When you reach the offer stage, clarify whether the role owns the platform roadmap or supports a separate ML engineering team. The distinction shapes your career trajectory, and it gives you a more informed foundation for any compensation conversation that follows.
AI Platform Engineer Jobs: Frequently Asked Questions
Which companies are hiring the most ai platform engineers?
The companies hiring the most ai platform engineers right now include Boston Consulting, Apple, and Adobe, with the largest share of openings in California, New York, and Texas, based on current listings on Migrate Mate as of June 2026. Demand is concentrated at organizations running large-scale model deployment programs across cloud, financial services, and enterprise software.
How many ai platform engineer jobs are remote?
About 38% of ai platform engineer openings are fully remote or hybrid as of June 2026, making it one of the more distributed engineering specializations. Roles focused on MLOps tooling, pipeline automation, and infrastructure-as-code tend to be the most remote-compatible, while positions tied to on-premise GPU clusters or regulated data environments are more likely to require on-site presence.
How do you become an ai platform engineer?
Start by building a strong foundation in software engineering and distributed systems, then layer in cloud infrastructure skills on at least one major provider. Learn containerization with Docker and Kubernetes, then move into ML-specific tooling by deploying a real model using an open-source pipeline framework. Contributing to open-source MLOps projects or maintaining a public portfolio of reproducible pipelines accelerates hiring consideration significantly.
Can you get an ai platform engineer job with little experience?
Entry-level ai platform engineer roles do exist, particularly at companies that treat the position as a specialized infrastructure role rather than a senior-only function. Candidates with strong DevOps or backend engineering backgrounds who have independently built and deployed a machine learning pipeline, even on a personal or open-source project, are competitive for these positions without prior professional ML infrastructure experience.
What does the ai platform engineer interview process look like?
The process typically begins with a recruiter screen focused on your infrastructure background, followed by a technical phone interview covering Python, distributed systems, or cloud architecture. Later rounds usually include a system design interview centered on ML pipeline or model serving architecture, a coding round emphasizing data structures and automation, and a final loop with engineering managers or platform leads assessing cross-functional collaboration.
Where can I find and apply to ai platform engineer jobs?
You can find and apply to ai platform engineer jobs on Migrate Mate, which lists current openings from employers across the United States. Find roles that match your background and apply directly to each listing.
See All 237+ AI Platform Engineer Jobs
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