Top AI Engineer Jobs

Build and operate production AI agent systems using Python, LangChain, LangGraph, and Amazon AgentCore. Develop agent tools, Snowflake integrations, retrieval workflows, structured outputs, evaluations, guardrails, and observability. Deploy, monitor, and improve reliable LLM-powered features while collaborating with data engineers. The role requires production Python experience, hands-on LLM development, evaluation framework design, and familiarity with modern AI tooling. Periodic travel may be required.

Design and develop secure, scalable Salesforce AI solutions, including Agentforce agents, multi-agent orchestration frameworks, custom MCP servers, and AI-client integrations. Build integrations using APIs and platform events, implement observability and security controls, translate business requirements into technical specifications, and support AI platform users. The role requires extensive Salesforce ecosystem experience, expertise with Data 360 and Headless 360, and hands-on Python or JavaScript development for AI and MCP services.

Design, build, and deploy enterprise-grade Generative AI and agentic AI platforms. Responsibilities include developing LLM, RAG, Graph RAG, vector search, multi-agent, MCP, data, backend, full-stack, cloud, inference, observability, LLMOps, security, and governance solutions. The role requires production deployment across AWS, Azure, and GCP, optimization for accuracy, latency, cost, scalability, and reliability, and collaboration with cross-functional technical and business teams.

Designs, deploys, and secures AI agents for enterprise risk and security operations. The role manages AI governance, Microsoft security and compliance controls, data protection, DLP, insider risk, access controls, monitoring, incident response, and SIEM/XDR integrations. It also leads security architecture, automation, AI risk evaluation, cross-functional initiatives, and mentoring while supporting secure adoption of Microsoft Copilot and Azure AI platforms.

Develop production-grade AI agents and reasoning workflows in Python, including memory management, sub-agent delegation, MCP server integrations, resilient execution pipelines, guardrails, testing, observability, and enterprise system connectivity. Collaborate with business and technology teams, coach junior engineers, review code, influence architecture, and ensure compliance, security, and quality standards across agile delivery squads.

Design and build production-grade AI agent and RAG systems for enterprise network automation. Responsibilities include agent orchestration, tool execution, memory, safety controls, LLM fine-tuning, evaluation, observability, reliability engineering, performance optimization, and production operations. The role also prototypes emerging AI technologies, diagnoses complex system failures, and collaborates across Engineering, Product, and QA to deliver scalable, secure AI capabilities.

Build and operate production-grade, cloud-native AI applications across the full stack. Responsibilities include integrating and optimizing AI models, developing evaluation and monitoring pipelines, operating reliable production systems, collaborating with stakeholders, leading design reviews, mentoring engineers, and participating in on-call rotations. The role supports proof-of-concepts, MVPs, and long-term product development while applying MLOps, LLMOps, DevOps, security, testing, and SRE practices.

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.

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.

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.

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.