Top AI Engineer Jobs

Build and evolve scalable generative AI software, including an agentic framework powering Atom AI and an AI asset portal. Develop production-ready web, data, and AI solutions; evaluate emerging models and frameworks; promote responsible AI practices; and ensure solutions are maintainable, cost-effective, and scalable. Partner with stakeholders, lead complex projects, demonstrate solutions, participate in strategy and ideation, and mentor other developers.

Lead end-to-end development of AI and machine learning solutions for a medical claims synthesis product. Design and deploy LLM-driven features, hybrid statistical ML and RAG systems, document extraction, and human-in-the-loop summarization workflows. Make architecture decisions, optimize production systems, evaluate model performance, collaborate with SMEs and product teams, and mentor engineers. Translate emerging AI research into reliable, scalable production applications.

Build and maintain backend infrastructure for AZX’s LLM gateway, including routing, rate limiting, key management, observability, sandboxing, Kubernetes services, operators, and autoscaling. Develop high-performance services in Go, Rust, or async Python using Envoy and gRPC. Support reliable model serving across cloud and customer-managed clusters, collaborate across platform teams, and help develop scalable inference and evaluation capabilities.

Develop and optimize advanced AI models, training pipelines, evaluation frameworks, and debugging systems for robotics applications. Lead rapid experimentation, model architecture development, fine-tuning, performance optimization, and interpretability efforts. Collaborate with engineering teams on training infrastructure and deployment. The role emphasizes ML planning for autonomous vehicles, diffusion and autoregressive models, reinforcement learning, and translating research into production systems.

Develop Python services, APIs, and AI-enabled production applications for enterprise use cases. Build data-centric solutions integrating enterprise systems, databases, external services, and AI techniques such as prompt orchestration, RAG, tool use, and agent workflows. Design scalable, secure architectures, implement authentication and authorization, contribute to React-based interfaces, and work across cloud and on-premises environments. The role also involves learning emerging technologies, collaborating with teams, and occasional customer-facing delivery.

Own the maintenance, security hardening, multi-tenant provisioning, deployment, and operation of an AWS-hosted AI SaaS platform. The role will also help commercialize the product through packaging, positioning, and go-to-market efforts. Applicants should have independently shipped and operated a real SaaS product and be comfortable working as the sole or near-sole engineer. Compensation is currently revenue share, equity, or another negotiated arrangement without guaranteed cash pay.

Design, build, deploy, and operate production-grade AI systems (agent platforms, assistants, evaluation frameworks) integrated with internal data and external services. Evaluate models and architectures, define metrics, ensure reliability, scalability, security, and observability, rapidly prototype new capabilities, and partner with trading and research teams to deliver measurable business impact.

Design, develop, and deploy NLP and conversational AI models for chatbot and enterprise systems. Build end-to-end ML pipelines, optimize model performance, integrate models into production with backend teams, document workflows, and stay current with AI advancements.

Lead workload-driven AI/ML systems architecture across hardware and software. Own workload characterization, analytical performance modeling, KPI definition, and hardware performance analysis to guide SoC, memory, and HW/SW co-design decisions. Evaluate compute, memory, latency, and software bottlenecks; collaborate with CPU, compiler, runtime, and ML framework teams; present recommendations to leadership; and mentor junior engineers. The role requires expertise in processor and memory architecture, edge AI acceleration, performance modeling, and cross-functional technical leadership.

Design, implement, and maintain AI platform components (model serving, RAG, prompt management, evaluation). Build APIs, observability, and integration patterns; collaborate with product teams, mentor junior engineers, and enforce compliance and data-handling standards for regulated financial environments.

Lead development of production agentic AI applications, translating business outcomes into agent specifications, tools, evaluations, and acceptance criteria. Build retrieval, orchestration, structured-output, approval, fallback, and failure-handling workflows using LLMs and APIs. Own evaluation harnesses, lead agile ceremonies, estimate work, and create sprint plans. Develop applications with Python or Java/Spring Boot and modern front-end technologies, while providing technical leadership and client-ready communication.

Design and ship governed autonomous-system frameworks and LLM applications for regulated enterprise clients. Own platform architecture, evaluation harnesses, multi-tenant identity and authorization, compliance controls, and AI unit economics. Mentor engineering leaders and partner with go-to-market teams on technical positioning and executive architecture narratives.

The Lead AI Engineer will design and develop scalable AI/ML solutions, focusing on automation, document retrieval, and decision-making processes while mentoring team members and collaborating with cross-functional teams.

The AI Engineer will build AI-driven workflows, integrating customer requirements into scalable solutions while managing both front-end and back-end elements in a cloud environment.

Develop and deploy multimodal foundation models and agentic AI for multi-robot coordination. Design training recipes, curate datasets, build simulation and RL environments, evaluate performance, and integrate models into real-time robotic systems with field trials.

Own developer-facing content and demos for Composio: build public demos using the API/SDK and LLMs/agents, grow social presence, represent the company at events, and partner with product/engineering to turn developer feedback into product.

Own and govern Microsoft Power Platform as a CoE lead, design and deliver AI-first automation (Power Apps, Power Automate, Copilot Studio), integrate with enterprise systems, enable citizen developers, monitor platform health, and ensure GxP-compliant operations and ALM for production-grade automations.

Administer, govern, and scale Microsoft Power Platform and AI automation across enterprise environments. Design and develop Power Apps, Power Automate flows, Copilot Studio agents, and integrations (Microsoft 365, Dataverse, APIs). Enable CoE adoption through training, governance, monitoring, ALM, and citizen-developer support while ensuring compliance, validation, and operational excellence in regulated settings.

Build and lead Replicant’s company-wide AI enablement program. Develop MCP integrations, shared agents, agent identity management, enterprise tool connections, reusable AI workflows, and cost-visibility mechanisms. Partner with Sales, Delivery, Finance, People, Engineering, and other teams to identify opportunities, implement solutions, promote AI literacy, and develop internal champions. Optimize AI systems through model routing, prompt caching, better agent design, and secure infrastructure.

Build Replicant’s internal AI enablement program by developing reusable agent workflows, MCP integrations, agent identity management, enterprise SaaS connections, and cost visibility mechanisms. Partner with Sales, Delivery, Engineering, Finance, People, and other teams to identify opportunities, implement secure AI solutions, create shared templates and skills, build AI literacy, and develop internal champions. Optimize AI infrastructure through prompt caching, model routing, tool consolidation, and improved agent design.

Build low-level device runtime software for OpenAI custom AI accelerators. Responsibilities include kernel scheduling, command submission, queueing, device memory management, synchronization, hardware-software interfaces, simulation-based validation, performance tuning, debugging, testing, tracing, and profiling. Collaborate with compiler, kernel, architecture, verification, firmware, and silicon teams to improve accelerator execution and co-design.

Designs, develops, and deploys end-to-end AI/ML solutions for financial services, including generative AI applications, LLMs, RAG systems, data pipelines, and production machine learning models. Builds AWS-based infrastructure and ETL workflows, performs model validation and risk assessments, establishes benchmarking and monitoring frameworks, and ensures compliance with banking regulations, privacy requirements, and ethical standards. Collaborates with technical and business stakeholders to identify AI opportunities and communicate complex concepts.

Designs and deploys AI assistants, copilots, custom agents, and end-to-end workflow automations across business functions. Builds integrations between AI platforms and enterprise systems using APIs, middleware, and serverless architectures. Develops prompt, RAG, and agentic solutions while measuring ROI and adoption. Partners with IT Operations and Security on governance, privacy, compliance, and secure deployment. Documents solutions, trains teams, and continuously improves automations based on usage and feedback.

Design, build, and deploy agentic AI systems, orchestration pipelines, and RAG/knowledge retrieval services. Implement scalable backend services, observability, LLMOps practices, and integrations with code repositories and enterprise systems. Mentor engineers and ensure explainable, traceable AI for regulated federal healthcare environments.

Partner with professional-services firms to discover and automate high-value workflows using Intapp Celeste. Deploy and iterate agents, playbooks, and semantic/ontology mappings; drive adoption through scoping, onboarding, training, and expansion; advise product and industry teams with real-world practitioner expertise. Act as a client-facing advisor and implementer, shaping product roadmaps while enabling Client Success to scale usage across a firm.