Top AI Engineer Jobs

Lead end-to-end front-end development for an enterprise Agentic AI platform. Design interactive, explainable UIs that render streaming LLM outputs, code citations, dependency graphs and authoring workflows. Build real-time experiences with WebSockets/streaming APIs, ensure traceability to source code, collaborate with AI engineers and designers, and drive accessibility, performance, and observability across a FedRAMP-aligned federal deployment.

The AI Engineer develops agent-based systems for automating workflows across various departments, working with AWS and AI frameworks. Responsibilities include building integrations, enhancing CodeAI capabilities, and supporting production systems.

Build and maintain a provider-agnostic LLM interoperability layer: transform OpenAI/Anthropic spec calls to other provider formats, implement session management, support provider features (skills, batching), and improve developer experience via the Python SDK. Work closely with founders and engage with users to ship production features.

Architect and build production-grade agentic AI systems and platforms using Google ADK, MCP, LangChain/LangGraph, and AlloyDB. Develop agent harnesses, RAG/GraphRAG pipelines, vector retrieval and memory stores, Terraform-based GCP infrastructure, CI/CD for model deployments, and enterprise security and governance for sessioned agent interactions.

Design, build, and operate production-grade AI systems (LLM applications, agent platforms, evaluation frameworks) using Python and modern AI frameworks. Translate ambiguous business problems into scalable, reliable AI solutions, define success metrics and evaluation pipelines, integrate with internal systems and third-party services, and continuously monitor and improve performance, reliability, cost, and safety in production.

Build and deploy production-grade AI agents within enterprise customer environments. Own solution definition, full-stack implementation, deployment, evaluation, and iterative performance improvement. Develop deep customer domain expertise, establish reusable engineering frameworks, collaborate with Product and Platform teams, and translate deployment insights into platform and product improvements.

Lead client engagements applying generative AI to business workflows, especially in regulated mortgage environments. Responsibilities include scoping use cases, building prototypes and proofs of concept, facilitating discovery sessions and learning labs, developing prompts and reusable solution artifacts, creating enablement materials, advising stakeholders on AI adoption, and collaborating with compliance and technical teams to scale successful experiments.

Build and deploy AI-enabled automation solutions across enterprise systems. Partner with Business Applications, Data & Analytics, and IT teams to analyze processes, identify automation opportunities, integrate platforms, test and monitor solutions, document implementations, and train process owners. The role requires hands-on coding, AI workflow experience, enterprise integrations, scripting, SQL, structured data knowledge, production support, and strong communication with technical and non-technical stakeholders.

Designs and maintains automated UI, API, end-to-end, and integration tests across React, Angular, and .NET applications. Validates data flows with SQL Server or Azure SQL, integrates test suites into Azure DevOps quality gates, and manages test data for parallel execution. Uses AI tools to accelerate test generation while critically reviewing coverage and assertion quality. Documents defects in Jira, contributes to test planning and acceptance criteria, and collaborates with engineers and offshore QE teams.

Deploy and optimize semiconductor EDA-to-ATE conversion flows using Velocity and related tools. Develop automation for STIL, WGL, EVCD, IJTAG, and SSN environments; support Advantest platforms; debug conversion, timing, protocol, and pattern issues; and create Python, Java, and related utilities. Collaborate with customer DFT and test engineering teams, support AI-assisted diagnostics and regression workflows, contribute to infrastructure improvements, and deliver global customer training and technical workshops.

Build and deploy AI-powered applications and agents for engineers and operators across the rocket manufacturing organization. Responsibilities include full-lifecycle software development, foundation-model integration, retrieval and vector search, evaluation, reliability, testing, continuous integration and deployment, and collaboration with users to define and iterate on solutions. The role uses modern AI, data, cloud, and platform technologies in manufacturing and physical-world environments.

Build and deploy AI-powered applications and agents used by engineers and operators. Responsibilities include full-cycle software development, foundation model integration, retrieval and tool use, evaluation, reliability, continuous delivery, and collaboration with non-software stakeholders. The role involves shipping production-quality systems across cloud and on-premise infrastructure while iterating on solutions for rocket manufacturing and launch operations.

Lead backend engineering for a cloud-native, data-intensive finance and P&L platform. Build and operate services (Java/Scala/Python) on Databricks and cloud, design secure scalable APIs, collaborate with finance and UI teams, and apply engineering best practices and AI/ML where valuable.

Defines enterprise AI architecture, technical strategy, standards, and roadmaps while remaining hands-on with prototyping and software development. Designs scalable LLM, retrieval, agentic, orchestration, evaluation, governance, and human-in-the-loop systems. Leads model and vendor decisions, architecture reviews, platform investments, and responsible AI practices across teams. Partners with executives and regulated-industry stakeholders, mentors technical leaders, and ensures secure, reliable, compliant, observable, and cost-effective production AI systems.

Lead the architecture and hands-on development of production AI systems using LLM pipelines, retrieval, agentic workflows, structured outputs, and human review. Establish standards for evaluation, reliability, observability, security, cost, and compliance. Guide technical design across projects, mentor engineers, manage production operations, and partner with product, clinical, and operational stakeholders to deliver scalable, auditable AI solutions in a regulated healthcare environment.

Develop, test, and release full-stack production features across backend, frontend, infrastructure, architecture, analytics, and integrations. Parse complex healthcare datasets into actionable insights, contribute to data modeling and cloud pipelines, and participate in code reviews and technical decisions. The engineer will own problems end to end, collaborate with customers and product teams, and work in a hybrid NYC environment.

Lead development of a patient-facing conversational AI agent for chronic disease management. Build scalable, low-latency voice and multimodal systems, engineer prompts, define evaluation metrics, develop fine-tuning data pipelines, deploy AI models and APIs, assess emerging LLM technologies, and collaborate with clinical experts to validate medical accuracy. Develop backend services using FastAPI and Pydantic while supporting secure, reliable medical AI infrastructure.

Lead SRE for AI platforms responsible for driving reliability culture, designing resilient systems, leading incident response, mentoring engineers, establishing SLOs/error budgets, applying AI-assisted operational workflows, and improving observability, CI/CD, container orchestration, and automation across services.

Lead architecture and production deployment of AI platforms across cloud and on-premises environments. Partner with enterprise architects, shape product strategy, resolve critical production issues, and implement scalable platform capabilities. Drive cross-functional alignment across Product, Engineering, DevOps, Security, ML, Customer Success, and Forward Deployed Engineering. Mentor senior engineers and improve deployment efficiency, reliability, security, observability, and scalability.

Design, build, and deploy AI-powered features for a digital banking platform, including natural language interfaces, assistants, and automated workflows. Integrate LLMs and AI APIs using prompt engineering, tool calling, validation, and safety guardrails. Evaluate AI outputs for accuracy, reliability, and regulatory compliance; collaborate cross-functionally; document best practices; conduct code reviews; and assess emerging AI tools and frameworks.

Design, build, and deploy enterprise-scale Generative AI and agentic applications using LLMs, RAG, vector search, and multi-agent architectures. Develop secure Python APIs and AI-powered web applications, create scalable ingestion and retrieval pipelines, and deploy cloud-native solutions across AWS, Azure, and GCP. Implement Docker, Kubernetes, Terraform, CI/CD, observability, LLMOps, governance, and responsible AI practices while integrating enterprise systems and collaborating with cross-functional stakeholders.

Build and operate production-ready LLM-powered agents for commercial banking customer service. Responsibilities include agent orchestration, tool and API integrations, RAG systems, knowledge pipelines, human-in-the-loop workflows, evaluation frameworks, safety controls, observability, CI/CD, and secure deployment. The role requires collaboration with product, operations, technology, risk, compliance, platform, and security teams in a regulated financial-services environment.

Leads the design, development, deployment, and governance of enterprise AI systems, including chatbots, RAG platforms, autonomous agents, and workflow automation. Architects secure solutions using major AI platforms and oversees OpenShift, Kubernetes, and CI/CD deployment pipelines. Establishes Python, TypeScript, and React engineering practices; guides prompt engineering, model evaluation, observability, and responsible AI controls. Manages and mentors engineering pods, coordinates Agile delivery, partners with stakeholders, and ensures cybersecurity, privacy, compliance, and model risk requirements are met.

Develop and maintain scalable Python applications for Generative AI, integrating APIs such as OpenAI and Gemini and implementing RAG systems. Build backend services with FastAPI, Flask, or Django; write tests; optimize performance, security, and scalability; and collaborate with product, data science, and engineering teams. The role also involves application lifecycle management, code reviews, user support, risk assessment, standards development, and mentoring junior analysts.

Develop and maintain scalable Python applications for Generative AI, integrating AI APIs and implementing RAG systems. Build backend services with FastAPI, Flask, or Django; optimize performance, security, and scalability; write tests; participate in code reviews; collaborate with product, data science, and engineering teams; and provide technical guidance to junior analysts.