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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Showing 5 of 826+ AI Platform Engineer jobs
Job Responsibilities:
- Collaborate with the team to design and develop high quality Web applications using Python, Flask, Django, and related technologies.
- Write clean and efficient code, and ensure code maintainability and reusability.
- Design and implement RAG pipelines on Google Cloud / Vertex AI (chunking, embeddings, indexing, retrieval, reranking, grounding).
- Build agentic workflows (tool use, planning, reflection/guardrails, structured outputs) using Python-first frameworks.
- Perform code reviews to ensure code quality and consistency.
- Conduct testing to ensure application quality and reliability.
- Create and maintain technical documentation for web applications.
- Participate in project planning, estimation, and prioritization.
- Stay up to date with the latest technologies for Python development.
- Define and run evaluation (retrieval metrics, answer quality, hallucination/grounding checks), and improve system quality iteratively.
- Ship to production: APIs, monitoring/observability, cost/performance optimization, CI/CD, and security best practices.
Requirement:
- Experience in software development in Python3.
- Decent understanding of the software development/testing life cycle.
- Knowledge of relational databases (e.g. MySQL, PostgreSQL, etc).
- Experience with version control tools, such as Git.
- Experience building RAG solutions (hybrid search, reranking, chunking strategies, embeddings, prompt + schema design).
- Familiar with at least one agentic framework (e.g., LangGraph/LangChain, LlamaIndex, Semantic Kernel, AutoGen) and tool/function calling patterns.
- Solid knowledge of vector search concepts and at least one vector DB in production.
- Strong engineering practices: code reviews, testing, telemetry, secure-by-design, reliability mindset.
Preferred Qualifications:
- Master’s Degree in Computer Science, Software Engineering, or related field.
- 1+ year professional experience in Python web application development with either Flask or Django.
- Experience in RESTful API development in Python.
- Understanding of Python web application frameworks such as Flask or Django.
- Experience with Cloud services, such as AWS.
- Experience with Vertex AI and GCP fundamentals (IAM, logging/monitoring, Cloud Run/GKE, storage).
- Knowledge graphs for RAG (entity linking, graph traversal + retrieval fusion).
- Streaming/messaging (Pub/Sub, Kafka), document pipelines (Document AI), and multilingual retrieval.
- Experience with evaluation tooling (RAGAS, TruLens, custom eval harnesses), prompt/version management.
- Frontend integration (basic React/Next.js) or platform enablement (internal developer tooling).
BeaconFire is an E-verified company and provides equal employment opportunities (visa sponsorship provided).
Job Responsibilities:
- Collaborate with the team to design and develop high quality Web applications using Python, Flask, Django, and related technologies.
- Write clean and efficient code, and ensure code maintainability and reusability.
- Design and implement RAG pipelines on Google Cloud / Vertex AI (chunking, embeddings, indexing, retrieval, reranking, grounding).
- Build agentic workflows (tool use, planning, reflection/guardrails, structured outputs) using Python-first frameworks.
- Perform code reviews to ensure code quality and consistency.
- Conduct testing to ensure application quality and reliability.
- Create and maintain technical documentation for web applications.
- Participate in project planning, estimation, and prioritization.
- Stay up to date with the latest technologies for Python development.
- Define and run evaluation (retrieval metrics, answer quality, hallucination/grounding checks), and improve system quality iteratively.
- Ship to production: APIs, monitoring/observability, cost/performance optimization, CI/CD, and security best practices.
Requirement:
- Experience in software development in Python3.
- Decent understanding of the software development/testing life cycle.
- Knowledge of relational databases (e.g. MySQL, PostgreSQL, etc).
- Experience with version control tools, such as Git.
- Experience building RAG solutions (hybrid search, reranking, chunking strategies, embeddings, prompt + schema design).
- Familiar with at least one agentic framework (e.g., LangGraph/LangChain, LlamaIndex, Semantic Kernel, AutoGen) and tool/function calling patterns.
- Solid knowledge of vector search concepts and at least one vector DB in production.
- Strong engineering practices: code reviews, testing, telemetry, secure-by-design, reliability mindset.
Preferred Qualifications:
- Master’s Degree in Computer Science, Software Engineering, or related field.
- 1+ year professional experience in Python web application development with either Flask or Django.
- Experience in RESTful API development in Python.
- Understanding of Python web application frameworks such as Flask or Django.
- Experience with Cloud services, such as AWS.
- Experience with Vertex AI and GCP fundamentals (IAM, logging/monitoring, Cloud Run/GKE, storage).
- Knowledge graphs for RAG (entity linking, graph traversal + retrieval fusion).
- Streaming/messaging (Pub/Sub, Kafka), document pipelines (Document AI), and multilingual retrieval.
- Experience with evaluation tooling (RAGAS, TruLens, custom eval harnesses), prompt/version management.
- Frontend integration (basic React/Next.js) or platform enablement (internal developer tooling).
BeaconFire is an E-verified company and provides equal employment opportunities (visa sponsorship provided).
AI Platform Engineer Jobs by Experience Level
Top Cities Hiring AI Platform Engineers
Explore AI platform engineer openings in the cities hiring most right now.
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Find AI Platform Engineer JobsAI Platform Engineer Job Market
Who's Hiring
- CVS Health57
- JPMorganChase18
- Thermo Fisher Scientific18
- Apple15
- GEICO15
Top Industries Hiring
- Technology & Software106
- Healthcare & Medical Services57
- Banking & Financial Services27
- Insurance23
- Consulting & Professional Services22
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 CVS Health, JPMorganChase, and Thermo Fisher Scientific, with the largest share of openings in California, Texas, and New York, based on current listings on Migrate Mate as of August 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 70% of ai platform engineer openings are fully remote or hybrid as of August 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 826+ AI Platform Engineer Jobs
Find roles that match your experience and apply in just a few clicks.
Find AI Platform Engineer Jobs