Sr. AI Engineer

Insight Global

Rogers, AR, United States

2 days ago

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Expert

Experience required

10 years minimum

Working hours

Regular working hours

Job source

www.techcareers.com

Tech stack

Application Programming Interfaces (APIs) Artificial Intelligence Amazon Web Services Automation of Tests Microsoft Azure Cloud Computing Code Review Continuous Integration Software Debugging Graph Database Python (Programming Language) Software Deployment

+12 more

Software Engineering Systems Integration Data Logging Enterprise Software Applications Large Language Models Generative AI Git AI Platforms Kubernetes Restful APIs Docker Microservices

Job description

We are seeking a Senior AI Engineer to lead the design and delivery of production-grade Generative AI applications, agents, RAG solutions, and orchestration workflows. This individual will personally build Python services, APIs, retrieval pipelines, evaluation frameworks, guardrails, and enterprise integrations rather than only supporting the API layer. The role will own a major AI project from requirements through production while managing technical risks, dependencies, milestones, and stakeholder communication. Qualified candidates should have 10+ years of software or AI engineering experience with hands-on knowledge of Python, LLMs, vector retrieval, LangChain or LangGraph, cloud deployment, containers, testing, monitoring, and CI/CD. The Senior AI Engineer will also provide technical direction, code reviews, and mentorship to other engineers while remaining an active contributor.

Requirements

10+ years of software or AI engineering experience with strong hands-on Python development skills.

Demonstrated experience building and deploying production-grade Generative AI applications.
Strong experience with LLM application development, RAG, embeddings, vector retrieval, agents, tool calling, and orchestration.
Experience building REST APIs, microservices, and integrations with enterprise applications and data sources.
Strong understanding of software engineering practices including automated testing, Git, code reviews, logging, debugging, and CI/CD.
Experience with Docker, containers, and cloud-based application deployment.
Experience implementing AI evaluation, monitoring, guardrails, and production troubleshooting.
Familiarity with frameworks such as LangChain, LangGraph, Semantic Kernel, or equivalent.
Experience with Azure OpenAI, AWS Bedrock, Google Vertex AI, or other enterprise AI platforms
Knowledge of GraphRAG, knowledge graphs, Kubernetes, infrastructure-as-code, AI observability, and Responsible AI practices is a plus.
Demonstrated ability to lead technical projects, mentor engineers, manage dependencies, and communicate effectively with technical and business stakeholders.