Top AI Engineer Jobs

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.

Support design, build, and deploy AI/ML models and AI-powered HR features (agents, chatbots, recommendations). Collaborate with HR, IT, and data teams, build HR data pipelines, document workflows, and handle sensitive HR data with confidentiality.

The Principal AI Engineer leads the development of AI-powered solutions, focusing on prompt engineering, guardrail design, and scalable applications. This role involves hands-on implementation, collaboration with teams, mentoring, and innovative problem-solving using cutting-edge AI technologies.

Design, build, and deploy agentic AI and LLM-based systems for life sciences applications. Lead research-to-production work on multi-agent systems, prompt optimization, evaluation pipelines, NLP/retrieval, observability, and cross-functional product delivery. Mentor engineers and represent the company in publications, conferences, and open-source contributions.

Lead design, development, fine-tuning, and deployment of LLM-based GenAI solutions (RAG, agents, prompt engineering). Own technical roadmaps, drive engineering delivery, mentor teams, integrate cloud deployments (Azure/GCP/AWS), maintain Python/SQL codebases (LangChain/LangGraph), and collaborate with product, data science, and business stakeholders.

The Forward Deployed AI Engineer designs, builds, and deploys intelligent applications using Python and related technologies, addressing customer needs and leading deployment projects.

Customer-facing Applied AI Engineer who builds demos, prototypes, and proofs-of-concept, troubleshoots production streaming and async API issues, leads technical discovery, advises on architecture and scaling, collaborates with Product/Research/Engineering, and travels for on-site workshops and conferences (~10-25% travel). Acts as voice of the customer and contributes documentation and open-source integrations.

Design and implement knowledge frameworks and RAG pipelines to integrate multimodal public health data, optimize retrieval and data quality, ensure privacy/anonymization, collaborate with data engineers, document architectures, and support AI agent and semantic search capabilities.

Design, prototype, and productionize end-to-end AI solutions: data ingestion, feature engineering, model development, deployment, APIs, and integration while ensuring security, monitoring, and responsible AI practices.