Top AI Engineer Jobs

Lead an embedded AI squad from discovery to production: harden and deliver AI prototypes, design reusable agentic AI patterns and architectures, ensure security/data governance, integrate AI into enterprise systems, codify reference implementations, mentor engineers, and measure outcomes to scale solutions across the organization.

The role involves developing AI-powered features, including agent capabilities, ensuring their reliability and scalability, and collaborating with cross-functional teams. Candidates should have strong hands-on experience with LLMs and Python in production environments.

Lead the development of AI applications, ensuring high reliability and performance while collaborating with teams to identify use cases and implement scalable solutions. Drive AI methodologies, governance, and continuous improvement in the organization.

As an Applied AI Engineer, you will enhance customer solutions using AI and machine learning, collaborating closely with clients to design effective AI implementations and drive business impact.

Lead client-facing AI engineering engagements: build and deploy production LLM systems using RAG, vector DBs, and agent frameworks; handle cloud deployments, CI/CD, and reliability; debug enterprise issues (auth, networking, data); embed with client teams and document solutions for reuse.

Lead a small AI engineering team to design, prototype, and deploy AI/LLM solutions for clinical workflows. Collaborate with Product and cross-functional teams, mentor junior engineers, and apply cutting-edge AI/ML research to production projects.

Lead architecture and implementation of autonomous inbound/outbound AI agents, build scalable LLM ops and signal-intelligence data pipelines, integrate identity resolution and third-party CRM systems, and maintain production-ready, well-documented systems for Series B diligence.

Design, build, and productionize agentic AI systems and RAG pipelines using LLMs and agent frameworks. Architect multi-agent workflows, integrate with enterprise data platforms, deploy cloud-native LLMOps stacks, implement observability, governance, and security, and mentor teams while shaping AI automation strategy.

The Senior AI Engineer will design and develop voice agents and agentic workflows for healthcare operations, ensuring reliable interactions and efficient backend processes while monitoring performance and iterating on systems based on user feedback.

Lead design and build scalable autonomous and semi‑autonomous multi‑agent AI systems. Implement RAG pipelines, tool‑calling agents, memory, observability, and AgentOps. Ensure production readiness, safety, governance, and secure data handling. Provide technical leadership, mentor engineers, and shape platform standards across teams.

Lead and manage an AI engineering team to design, develop, and integrate machine learning and deep learning solutions across business units, provide technical leadership and best practices, collaborate with cross-functional teams, and stay current with AI advancements to drive continuous improvement.

Design, build, and deploy production-grade Generative AI applications: multi-agent systems, advanced RAG pipelines, LLM integration and swapping, model fine-tuning and quantization, scalable FastAPI backends, containerized deployment, and monitoring/optimization for latency, cost, and reliability.

Build and deploy generative AI solutions: develop production-grade Python code, LLM-based systems, agent frameworks, RAG pipelines with vector databases, APIs, cloud deployments, and MLOps practices while ensuring model evaluation, guardrails, and observability.

Design, build, and deploy agentic AI and multi-agent systems using LangChain/LangGraph and ADKs. Implement LLM orchestration, RAG pipelines, vector DBs, React-based AI interfaces, and production-grade infrastructure with observability, security, and optimization.

Design, develop, and optimize prompts for conversational AI and integrate them into NLP/LLM systems. Build RESTful APIs (Python/Django/Flask) and React front-ends, evaluate prompt effectiveness with metrics and user feedback, collaborate with product, UX, and data teams, and document prompt engineering methodologies.

Build and maintain scalable full-stack web applications using Spring Boot or Node.js and Python. Develop responsive front-ends, RESTful APIs, integrate AI/ML models, manage SQL/NoSQL databases, and implement CI/CD with GitHub Actions while collaborating with cross-functional teams.

Design and implement agentic, LLM-powered chat systems and workflows using LangGraph/LangChain, vector databases, and RAG. Build scalable, secure architectures, reference implementations, and documentation; collaborate with product and engineering teams; ensure non-functional requirements and responsible AI practices; mentor engineers and promote GenAI best practices.

The AI Engineer will design backend systems for workflows, optimize multi-agent pipelines, develop knowledge retrieval systems, and collaborate to integrate workflows using LLMs and traditional systems.

Build and deploy production AI capabilities for mental healthcare: full‑stack development, agentic systems, RAG and retrieval pipelines, evaluation frameworks, safety guardrails, observability, CI/CD, and cross‑functional collaboration to ensure safe, reliable AI in clinical workflows.

The Applied AI Engineer will work at the intersection of product, engineering, and research to develop AI tools, influence engineering practices, and contribute to company culture while addressing customer challenges.

Build production-grade AI agents and RAG pipelines that integrate with internal tools (BookStack, Slack, Asana, Gmail). Create REST APIs, ingestion pipelines, and function-calling workflows; monitor and iterate on agent performance while collaborating with backend (.NET) and frontend (Angular) teams.

Build AI-driven developer tools: coding assistants, knowledge retrieval, workflow automation, and LLM-powered apps for code generation, understanding, and test automation. Collaborate with engineering teams to identify pain points, ship solutions, and experiment with prompt engineering, RAG, and AI agents.

Build internal AI developer productivity tools (coding assistants, retrieval systems, workflow automation). Develop LLM-powered apps for code generation, understanding, and test automation. Integrate AI across the SDLC, embed with teams to remove friction, experiment with prompt engineering, RAG, and AI agents, and evaluate emerging AI technologies.

Lead and deliver an enterprise AI platform and agent factory: design architecture, build cost-effective model infrastructure, harden security/SSO/IAM, enable developer adoption, and build agent workflows across the PDLC (ideation, development, deployment). Mentor engineers, steer delivery partners, and remain hands-on implementing agents, RAG pipelines, integrations, and observability to drive measurable productivity gains.

The Full Stack AI Developer will design and scale AI-driven platforms, develop CI/CD pipelines, manage deployments, and collaborate with teams.