Top AI Engineer Jobs

Lead development of a production-grade AI platform for federated agentic workflows and sales assistants. Design, prototype, and deploy multi-step LLM agents with orchestration, routing, memory, retrieval, evaluation, and observability capabilities. Evaluate emerging AI techniques, improve quality through feedback loops and error analysis, establish safe deployment standards, and partner with product, engineering, quant research, vendors, and startups. The role requires hands-on Python development, AWS experience, and expertise in LLM agents, RAG, tool use, prompting, and evaluations.

Designs, develops, and deploys secure, scalable AI solutions using LLMs, AI agents, RAG, prompt engineering, and cloud-native technologies. Builds data pipelines, APIs, and AI applications; evaluates model outputs; implements governance, security, and compliance controls; and supports CI/CD, documentation, model registration, and AI roadmap execution. Collaborates with technical and business stakeholders to deliver reusable AI capabilities and improve member experiences.

Designs, develops, tests, and maintains AI-enabled applications and agent workflows using Python, LangChain, Microsoft Agent Framework, RAG, APIs, and cloud AI services. Integrates models with enterprise data and applications, builds evaluation and testing processes, supports CI/CD deployment and monitoring, and applies security, privacy, and responsible AI practices. Collaborates with product, data, cybersecurity, infrastructure, and functional teams to deliver secure, reliable solutions for federal mission needs.

Leads architecture, development, and deployment of agentic workflows for banking use cases. Integrates agents with core banking systems, databases, microservices, and event streams. Establishes security controls, mentors engineers, conducts code and architecture reviews, assesses risks, and contributes to enterprise agent blueprints and reusable components.

Optimize LLM inference on modern GPUs by analyzing GPU architecture, memory access, kernels, and performance bottlenecks. Develop, debug, reverse-engineer, and optimize CUDA kernels and low-level GPU operations for matrix multiplication, attention, and neural network layers. Benchmark latency, throughput, and utilization using frameworks such as vLLM, SGLang, TensorRT-LLM, Triton, and FlashAttention. Investigate technically challenging AI systems and high-performance computing problems.

Advise life sciences and biotech startups on building products and research workflows with Claude. Partner with engineering and scientific teams through architecture reviews, pair programming, prototyping, evaluations, and technical documentation. Develop reference implementations, MCP servers, agent skills, benchmarks, and scalable architectures for scientific applications. Share deployment insights with Anthropic product and research teams, create developer content, engage the startup ecosystem, and travel to customer sites for workshops and implementation support.

Serve as a technical advisor to physical AI startups across robotics, aerospace, semiconductors, manufacturing, and civil infrastructure. Partner with software and hardware teams on Claude integrations, agent architectures, prototypes, evaluation frameworks, reference implementations, and technical documentation. Identify recurring engineering challenges and develop scalable solutions, while supporting workshops, hackathons, events, and customer-site engagements.

Leads the design, development, evaluation, and production operation of healthcare AI products, including agentic workflows, RAG systems, model optimization, guardrails, and human-review controls. Partners with product, clinical, engineering, data, platform, and security teams to build safe, reliable, observable, and compliant AI systems. Provides technical leadership through architecture, code reviews, incident response, mentoring, and delivery of HITRUST-aligned solutions for regulated healthcare environments.

Build and deploy production AI solutions that improve business workflows, including AI agents, copilots, RAG applications, automations, and decision-support tools. Partner with cross-functional teams to identify opportunities, translate requirements, integrate enterprise systems and data sources, establish testing and governance, support adoption, and continuously improve solutions. The role requires hands-on software engineering, modern AI development, cloud integration, evaluation, deployment, and stakeholder communication.

Develop and support enterprise Generative AI applications, LLM integrations, RAG solutions, AI agents, chatbots, and intelligent automation using Python and cloud AI services. Build APIs, data pipelines, and vector database integrations; support model evaluation, deployment, monitoring, MLOps, and LLMOps. Collaborate with technical and business teams, troubleshoot systems, document solutions, and participate in Agile development.

Develop, optimize, validate, deploy, and monitor AI and machine learning models across cloud, edge, and hybrid environments. Design deployment pipelines and MLOps workflows, integrate models with mission and enterprise systems, evaluate architectures, support retraining, and detect model drift. Collaborate with data, cloud, network, and security engineers to deliver reliable, scalable solutions for mission-critical operations.

Build and operate shared platform services for production AI agents, focusing on orchestration, runtime execution, integrations, observability, reliability, testing, and workflow automation. Partner across engineering, product, QA, operations, and support teams to release reliable agent capabilities. Improve developer tooling, monitoring, debugging, and deployment practices; support incident response and continuous improvement; contribute to technical designs, code reviews, rollout plans, and mentoring.

Builds AI-agent evaluation harnesses, golden datasets, synthetic benchmarks, regression testbeds, prompt templates, structured output parsers, and automated CI/CD testing pipelines. The role analyzes agent performance, latency, hallucinations, and tool-call accuracy; implements security guardrails; supports evaluation cycles; and provides diagnostic reporting to stakeholders. It requires independent judgment, advanced Python development, and expertise in generative AI application testing.

Senior individual contributor leading applied AI engineering from algorithm scoping through production deployment. Responsibilities include building relational models for AI agents, developing persistent agent memory and grounding layers, shaping platform architecture, owning complex technical projects, and coaching AI engineers. The role requires production experience with machine learning and LLM systems, agent-based AI expertise, traditional ML knowledge, and a master’s degree in computer science. This is an on-site position in Redwood City, California.

Build and deploy production GenAI systems, agentic workflows, retrieval pipelines, and evaluation harnesses for enterprise customers. Work directly in customer operations to understand and redesign business processes before automating them. Write production code across backend, data, and AI layers; deploy maintainable systems on AWS; drive adoption; and own outcomes tied to business KPIs. The role requires senior technical leadership, domain adaptability, stakeholder credibility, and hands-on engineering.

Leads capital projects in regulated Life Sciences manufacturing and quality environments. Responsibilities include planning, scheduling, budgeting, risk management, vendor coordination, stakeholder communication, and cross-functional team leadership. Ensures compliance with GMP, FDA, GxP, validation, and internal quality requirements while managing technical platforms and project delivery across IT, automation, quality, engineering, and validation teams. This is a full-time onsite role in Ohio.

Design, build, deploy, and optimize scalable AI and machine learning systems using large datasets. Develop predictive models, recommendation systems, personalization engines, forecasting solutions, generative AI applications, and production ML pipelines. Collaborate with data, backend, and product teams to integrate models into applications, while monitoring performance, data quality, drift, reliability, and cost. Establish best practices for evaluation, experimentation, reproducibility, and deployment, and research emerging AI technologies.

Build and operate production agentic AI systems, including prompts, tools, retrieval, guardrails, evaluations, observability, and rollout. Automate expert workflows, ensure compliance and safety, and establish human-in-the-loop controls. Deliver documentation, runbooks, dashboards, and evaluation harnesses while enabling teams to adopt AI safely. The role requires substantial software engineering experience, production LLM deployments, retrieval expertise, structured outputs, local model experimentation, and strong communication.

Lead the design and delivery of production AI infrastructure, including context-aware data layers, configurable autonomous agents, event-driven automations, model-agnostic inference, access controls, and workflow governance. Contribute approximately 30% of the time to full-stack product engineering and on-call support. Partner with founders on architecture, technical direction, and engineering standards.

Lead the technical direction of an AI conversational product, including agent architecture, model selection, reasoning, tool-calling, memory, evaluation, and monitoring. Build reliable browser automation and full-stack features with TypeScript and Node.js. Collaborate with early customers and engineering leadership to refine product capabilities, manage latency and costs, and deliver AI agents from concept through production.

Lead production AI engineering for a software product, owning AI feature design, model integration, data flows, evaluation, deployment, monitoring, and support. Build automated quality checks, optimize latency and costs, investigate model, data, and interface issues, and release weekly. Establish long-term architecture and serve as the team’s model-related technical expert.

Design and deploy LLM-driven agent applications that automate complex, document-heavy financial processes. Own the full AI product lifecycle, including prototyping, evaluation, release, monitoring, and continuous improvement. Collaborate with founders and subject-matter experts to identify workflow weaknesses, improve agent context and design, and create intuitive user experiences. Translate ambiguous problems into measurable production solutions and influence the product roadmap through hands-on applied AI work.

Own the machine learning layer of a production software product, including AI feature design, model integration, data pipelines, evaluation tooling, testing, deployment, performance optimization, and monitoring. Diagnose whether issues originate in models, data, or user experience, and make foundational architecture decisions while releasing improvements weekly.

Build and scale reliable agentic AI systems, including orchestration, code execution, agent memory, LLM self-improvement, simulations, knowledge graphs, and multimodal interfaces. Implement AI algorithms, productionize them, optimize performance, write robust code, contribute to technical roadmaps, collaborate across engineering and product teams, and mentor engineers. The role requires strong production-systems experience, technical communication, operational excellence, and programming in Python, Golang, and/or Java.

Senior Full Stack Engineer responsible for architecting and delivering AI-driven backend and frontend systems. The role leads complex technical initiatives, defines roadmaps, designs scalable APIs and data models, and champions JavaScript, Node.js, testing, observability, and Spec-Driven Development. It requires building and scaling distributed, event-driven microservices; working with financial infrastructure; mentoring engineers; and translating ambiguous business objectives into reliable technical solutions.