AI Solutions Engineer Jobs
AI Solutions Engineer jobs are open across enterprise software, financial services, healthcare, and manufacturing, from associate to staff and principal level, with specializations in LLM integration, MLOps, and enterprise AI deployment. Find a role that fits from the openings below and apply directly.
Find AI Solutions Engineer JobsLooking for remote work? View remote AI solutions engineer jobs →Overview
Showing 5 of 816+ AI Solutions 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 Solutions Engineer Jobs by Experience Level
Top Cities Hiring AI Solutions Engineers
Explore AI solutions engineer openings in the cities hiring most right now.
See All 816+ AI Solutions Engineer Jobs
Find roles that match your experience and apply in just a few clicks.
Find AI Solutions Engineer JobsAI Solutions Engineer Job Market
Who's Hiring
- Amazon Web Services57
- NVIDIA21
- Amazon21
- CVS Health19
- Booz Allen Hamilton14
Top Industries Hiring
- Technology & Software93
- Consulting & Professional Services35
- Distribution & Wholesale19
- Investment & Asset Management18
- Banking & Financial Services16
What Employers Look For
The qualifications that appear most often in AI solutions engineer jobs.
- Bachelor's or master's degree in computer science, data science, or a related engineering field
- Hands-on experience with large language model APIs such as OpenAI, Anthropic, or Google Gemini
- Proficiency in Python and at least one cloud platform including AWS, Azure, or GCP
- Experience designing and deploying retrieval-augmented generation or agentic AI pipelines
- Familiarity with MLOps tooling for model versioning, monitoring, and continuous evaluation
- Strong written and verbal communication skills for translating technical solutions to business stakeholders
Tips for Your AI Solutions Engineer Job Search
Tailor your resume to the stack
Recruiters screening ai solutions engineer resumes look for specific frameworks like LangChain, Azure OpenAI, or Vertex AI. List the exact model serving and orchestration tools you've used rather than generic AI or ML experience, so your resume clears automated filters.
Build a demo-ready technical portfolio
For ai solutions engineer roles, a working demo of an LLM-powered application or RAG pipeline carries more weight than a list of technologies. Host a short walkthrough on GitHub or a public repository so hiring managers can evaluate your architectural decisions directly.
Target openings by deployment environment
AI solutions engineer postings vary widely between cloud-native roles (AWS, Azure, GCP) and on-premises enterprise deployments. Filtering by the deployment environment that matches your hands-on experience keeps your applications tightly matched to roles you can speak to confidently.
Apply early to roles that fit
Migrate Mate lists ai solutions engineer openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Prepare for architecture whiteboard questions
Interviews for ai solutions engineer positions routinely include live design exercises around retrieval-augmented generation, prompt engineering pipelines, or model evaluation workflows. Practice articulating latency tradeoffs, context window constraints, and fallback strategies before you get on the call.
Negotiate scope before you negotiate compensation
In ai solutions engineer offers, the definition of the role, whether you own model selection, integration, or post-deployment monitoring, affects your growth path significantly. Clarify ownership boundaries in the final interview round so you enter salary negotiation with the full picture.
AI Solutions Engineer Jobs: Frequently Asked Questions
Which companies are hiring the most ai solutions engineers?
The companies hiring the most ai solutions engineers right now include Amazon Web Services, NVIDIA, and Amazon, 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 in enterprise software vendors, large financial institutions, and cloud-platform providers building out customer-facing AI products.
How many ai solutions engineer jobs are remote?
About 59% of ai solutions engineer openings are fully remote or hybrid as of August 2026, making it one of the more remote-accessible roles in applied AI. Sub-areas most commonly listed as fully remote include LLM integration, prompt engineering, and cloud-based AI consulting work, while on-premises enterprise deployment roles tend to require more in-person presence.
How do you become an ai solutions engineer?
Start by building a foundation in software engineering and cloud infrastructure, then move into applied machine learning or NLP work. Gain hands-on experience with at least one major LLM API, build end-to-end projects that connect a model to a real business workflow, and document those projects publicly. Roles labeled solutions engineer, AI engineer, or ML engineer in enterprise settings are common entry points for people transitioning from software or data engineering.
Can you get hired as an ai solutions engineer without direct AI experience?
Yes, particularly if you have a strong software engineering or cloud architecture background and can demonstrate AI-adjacent project work. Employers hiring at the associate or mid-level often prioritize system design skills and the ability to integrate APIs over deep model training experience. Building a public project using an LLM API and presenting it clearly in interviews compensates meaningfully for a shorter AI-specific work history.
What does the ai solutions engineer interview process look like?
Most processes run three to five rounds and typically begin with a recruiter screen focused on your deployment background and tool familiarity. A technical phone screen or take-home follows, often involving an API integration task or architecture design scenario. Final rounds usually include a live whiteboard or system design session and a cross-functional presentation to non-engineering stakeholders, testing your ability to translate AI concepts into business outcomes.
Where can I find and apply to ai solutions engineer jobs?
You can find and apply to ai solutions engineer jobs on Migrate Mate, which lists current openings from across the United States. Find roles that match your background and apply directly to each listing to get your application in front of the hiring team.
See All 816+ AI Solutions Engineer Jobs
Find roles that match your experience and apply in just a few clicks.
Find AI Solutions Engineer Jobs