Top AI & Machine Learning Jobs

Build and operate production AI applications and shared platform capabilities using AWS Bedrock and Python. Develop AI agents, MCP servers, APIs, and distributed services; integrate AI into existing applications; evaluate retrieval, tool-use, orchestration, and model-performance approaches; and ensure reliability, security, testing, observability, and maintainability. Partner with technical and business stakeholders to identify workflow improvements, influence architecture and engineering standards, support delivery planning, and mentor less experienced engineers.

Build and operate applied AI systems, including LLM integrations, agentic workflows, data pipelines, evaluation frameworks, MCP tools, APIs, and governed content-serving layers. The role emphasizes production reliability, provenance, security, cost and latency management, regression detection, and human-reviewed AI outputs. Responsibilities include cloud deployment, structured content modeling, telemetry, governance, mentoring, and collaboration on AI standards. Strong Python, TypeScript, AWS, API, data pipeline, and production LLM experience are required.

Build and operate production applied AI systems, including LLM integrations, agentic workflows, data pipelines, evaluation frameworks, APIs, MCP tools, and governed content-serving layers. Develop reliable extraction and classification agents, model evaluation harnesses, telemetry, and structured data pipelines. Ensure security, privacy, provenance, reliability, latency, and cost controls while mentoring engineers and contributing to AI governance and standards.

Lead and grow an ML engineering team to design, build, and operate a unified AI/ML data collection platform. Own data ingestion, feature management, LLM/RAG workflows, model lifecycle, evaluation, observability, scalability, governance, and cross-functional alignment while mentoring engineers and driving platform reliability and operational excellence.

Lead AI use-case discovery, design, prototyping and delivery. Partner with business leaders to frame outcomes, design architectures (RAG, LLMs, cloud AI), build MVPs using Python/APIs and low-code tools, and drive adoption with governance and responsible AI practices.

Build and own end-to-end NLP and ML solutions (including LLMs, RAG, embeddings) to extract structured information from documents. Responsibilities include problem definition, data exploration, model development, evaluation, productionization, monitoring, and cross-team collaboration to improve data quality and coverage.

Design, build, and operationalize a unified AI/ML data collection platform. Responsibilities include LLM/RAG pipelines, data ingestion, feature management, model lifecycle and deployment, observability, scalable distributed systems, and mentoring engineers to deliver production-ready ML solutions.