Top AI Engineer Jobs

Design, build, and deploy production-grade AI systems focused on Generative AI and LLM applications. Develop RAG pipelines, fine-tune models, build scalable APIs, optimize inference, and implement vector search, observability, guardrails, and responsible AI practices. Contribute to MLOps, cloud deployment, model evaluation, and reliable enterprise AI solutions while partnering cross-functionally on industrial use cases.

Build and productionize physical AI systems involving agentic AI, machine learning, reasoning, knowledge graphs, multimodal data, forecasting, optimization, and autonomous decision-making. Develop models, prototypes, and scalable software while collaborating with researchers, product teams, and engineers. The role emphasizes experimentation, reliable deployment, and solving real-world industrial problems using data from sensors, documents, images, video, and enterprise systems.

Designs, develops, deploys, and monitors production-grade machine learning and Generative AI solutions, with emphasis on LLMs, transformer architectures, RAG, model evaluation, observability, and responsible AI. Builds scalable ML pipelines, conducts experimentation and optimization, and collaborates with Product, Engineering, and business stakeholders to solve complex industrial problems involving forecasting, anomaly detection, demand planning, renewable energy, and predictive maintenance.

Design, build, deploy, and own production AI systems for industrial customers. Engineers implement computational knowledge graphs, replace static workflows with autonomous real-time applications, collaborate directly with operators and stakeholders, deploy systems into high-stakes operational environments, and feed field learnings into platform evolution.

Build and own the Learn Engine platform and simulation layer, including campaign simulation, experimentation infrastructure, screen-capture and creative-intelligence pipelines, and client-facing data products. Develop tools that model campaign outcomes before spending, transform complex workflows into usable products, and create closed-loop optimization capabilities. The role emphasizes high-velocity AI-first development, platform ownership, simulation fidelity, and productized research rather than optimization policy design.

Build the intelligence and optimization layer for an autonomous growth platform managing advertising spend. Develop recommendation engines for bid changes, budget allocation, campaign pause or boost decisions, and postback optimization. Design scoring systems, optimization policies, experimentation approaches, LLM-driven agents, and closed-loop learning systems that improve from campaign outcomes. Own decision quality and continuous strategy improvement rather than platform infrastructure or simulator development.

Build and operate production AI capabilities using Python, LLMs, retrieval, and intelligent automation. Responsibilities include integrating LLM APIs, developing retrieval-augmented features, creating evaluation and prompt frameworks, applying security and compliance controls, monitoring production systems, and collaborating across engineering, data, security, product, and operations teams.

Leads the architecture, development, and operation of Vertex’s enterprise agentic AI platform. Responsibilities include building RAG and knowledge retrieval systems, orchestration services, AI gateways, control towers, governance and guardrails, model routing, FinOps, observability, evaluation frameworks, and lifecycle management. The role defines technical standards and roadmaps, ensures scalability and security, partners across functions, evaluates emerging technologies, and mentors engineers while remaining hands-on.

Architects and builds production agentic AI systems for Azure customer support, including LLM orchestration, multi-agent workflows, RAG, tool integrations, evaluation frameworks, and secure AI services. Writes production C# and Python, owns technical delivery and reliability, establishes engineering standards, leads architecture decisions, responds to incidents, mentors engineers, and influences cross-team product and platform roadmaps.

Design, build, and deploy AI systems in classified and disconnected government environments. Develop RAG applications, adapt open-weight language models, serve models on GPU infrastructure, and implement secure ingestion, access controls, evaluation, guardrails, and compliance processes. Translate mission needs into AI architectures, guide technology adoption, collaborate across engineering and security teams, and communicate technical tradeoffs to government leadership.

Design, build, and deploy secure AI capabilities for classified and disconnected Government environments. Responsibilities include developing RAG applications, adapting open-weight language models, serving models on GPU infrastructure, building document ingestion and access-controlled retrieval pipelines, and implementing evaluation, guardrails, and prompt-injection defenses. The role translates mission needs into AI architectures, supports ATO and compliance requirements, evaluates emerging technologies, and collaborates with engineering, security, mission teams, and Government leadership.

Design, build, and operate production agentic AI services using LLM orchestration, RAG, tool integration, evaluation frameworks, and secure AI patterns. Write production C# and Python, integrate Azure services, and own solutions through deployment, monitoring, incident response, and continuous improvement. Lead technical execution, mentor engineers, and collaborate with product, research, platform, and support teams to improve customer support automation, reliability, and resolution outcomes.

Build and deploy production generative AI systems for Typeform, including LLM applications, RAG, agentic workflows, APIs, machine learning pipelines, and evaluation systems. Design scalable infrastructure using Python, AWS, Docker, Kubernetes, Kafka, Airflow, and MLflow. Improve AI quality, reliability, security, performance, and cost through automated evaluation, monitoring, safeguards, and standardized engineering practices. Collaborate with Product, Engineering, Data Science, Data Engineering, and Analytics teams.

Lead the technical direction and hands-on delivery of Typeform’s generative AI systems, including Research Flow, enterprise RAG, agentic workflows, model evaluation, ML pipelines, and scalable infrastructure. Design and operate reliable production services, establish AI quality and security standards, mentor engineers, guide cross-team initiatives, and connect technical decisions to customer outcomes.

Build and operate production agent infrastructure, including agent harnesses, sandboxed execution environments, context systems, observability, evaluation suites, and model-routing systems. Work directly with customers to turn prototypes into reliable products, then use production traces to improve environments, reward signals, and models. Own technical decisions across agent reliability, cost, latency, security, and scale in a founding startup role.

Build and support a production conversational AI system for fleet analytics. Responsibilities include extending LangGraph agents and domain tools, improving RAG retrieval, supporting production incidents, optimizing LLM cost and latency, developing evaluations and testing gates, and strengthening tenant isolation, authorization, and prompt-injection defenses. The role requires strong Python, PostgreSQL, LLM orchestration, observability, testing, and production-service experience, with Azure, vector search, guardrails, and frontend skills preferred.

Build AI-driven pipelines and automated validation frameworks for radar, RF, sensor, software, firmware, and FPGA systems. Develop anomaly detection, root cause analysis, LLM-powered telemetry interpretation, monitoring dashboards, and CI/CD-integrated testing workflows. Collaborate with hardware and systems engineers to improve defense and sensing products. The role requires production-grade Python, sensor-data expertise, hardware-in-the-loop experience, and on-site work in El Segundo, California.

Build and operate production-grade AI agents that automate enterprise workflows and make bounded decisions. Design multi-agent architectures, RAG pipelines, tool integrations, governance controls, evaluations, monitoring, and secure deployments across business systems. Collaborate with technical and business stakeholders to identify automation opportunities, validate outcomes, manage risk, and improve AI solutions. Mentor engineers and develop reusable standards, documentation, and enablement materials.

Leads the architecture, development, optimization, and production delivery of complex AI/ML and agentic systems. Builds scalable ML platforms, pipelines, and infrastructure; establishes engineering standards; drives AI strategy; and mentors engineers. The role requires expertise in generative AI, AWS, RAG, evaluation and observability, AI safety, API integration, cost optimization, Docker, Kubernetes, and AI coding tools.

Designs, builds, deploys, evaluates, and optimizes production AI systems, including RAG pipelines, agentic workflows, and model integrations. Uses Python and cloud platforms to deliver scalable solutions, establishes quality and monitoring frameworks, addresses responsible AI and security concerns, and manages cost-performance tradeoffs. Serves as a senior technical client advisor, supports discovery and presales, mentors engineers, researches emerging AI technologies, and develops reusable accelerators.

Designs and deploys enterprise AI applications and Copilot Studio agents using Azure AI, Azure OpenAI, Python, and Power Platform. Builds RAG solutions, APIs, workflows, and responsible AI safeguards; integrates Microsoft 365 and enterprise data. Supports proofs of concept through production, establishes engineering and deployment practices, tracks solution impact, and guides makers and stakeholders on AI adoption.

Architects enterprise data warehouse and orchestration platforms, establishes data governance and semantic layers, and builds production RAG, LLM, and MLOps systems. The role also develops machine learning models for portfolio construction, evaluates AI tools, supports security audits, manages Python package publishing, and coordinates technical projects with stakeholders.

Deploy and support industrial AI and digital manufacturing solutions across manufacturing sites. Translate plant needs into system requirements, coordinate implementation partners, manage data and equipment readiness, integrate AI systems with SAP, MES, EWM, and factory equipment, execute testing and deployment, and provide operational handover and troubleshooting. The role supports machine vision inspection, digital twins, edge computing, performance monitoring, and ongoing model or configuration updates.

Lead the technical direction, development, evaluation, security, observability, and cost optimization of production LLM agent systems. Build shared agent infrastructure, improve reliability and fault tolerance, establish testing and rollout standards, and partner with engineering, product, research, and security teams. Mentor engineers, support AI coding-agent adoption, investigate incidents, and guide model selection and production readiness across teams.

Build and operate production-grade agentic AI systems for enterprise SaaS, including orchestration, MCP tools, memory, RAG, knowledge retrieval, security guardrails, evaluation, and observability. Own products from prototyping through production, make tradeoffs among accuracy, latency, reliability, and cost, support incident response, create reusable tooling, and mentor engineers. The role requires expert Python, cloud-native infrastructure, distributed systems, and hands-on LLM production experience.