Top AI Engineer Jobs

Administer and improve enterprise AI platforms including Microsoft Copilot, Azure AI Foundry, ChatGPT Enterprise, and Claude Enterprise. Configure security, access, licensing, integrations, governance, monitoring, and cost controls. Investigate complex issues, perform root-cause analysis, develop technical standards and runbooks, manage platform initiatives, evaluate emerging technologies, and support responsible AI adoption. Collaborate with security, architecture, engineering, governance, and business teams while mentoring junior staff and providing platform guidance.

Lead design and deployment of physics-based, ML, and LLM-powered modeling solutions for semiconductor process, packaging, and manufacturing. Architect agentic AI systems integrated with modeling/design tools and fab/test platforms, improve model performance (benchmarking, RLHF, prompt tuning), and translate results into clear visual insights for diverse audiences.

Leads the design, development, and scaling of AI-enhanced applications, APIs, microservices, and automation frameworks. Integrates LLMs, generative AI, computer vision, chatbots, agentic workflows, and autonomous systems into production products. Establishes AI-first engineering practices, optimizes model costs and performance, implements observability, and mentors engineers. The role also influences architecture, technical strategy, business trade-offs, and adoption of AI-powered development tools in an agile environment.

Own the company’s AI-powered quality and release infrastructure as its first dedicated hire. Build agent-run test harnesses, LLM graders, regression coverage, release gates, fresh-install and upgrade verification, and natural-language UAT. Manage CI health, artifact publication, deployment pipelines, credentials, certificates, and staging environments. Write production Python code, convert escaped bugs into regression tests, and provide trusted release-readiness evidence through dashboards, runbooks, retrospectives, and operating policies.

Build and ship the company’s second major AI product across backend APIs, dashboards, and developer experience. Work directly with founders and customers to identify problems, prioritize features, and translate feedback into production software. The role requires ownership of ambiguous problems, rapid execution, and close collaboration in a high-growth startup environment.

Lead technical pre-sales for enterprise healthcare data and AI deals. Own discovery, solution design, RFPs, security reviews, proof-of-value programs, data-delivery scoping, and technical evaluations. Configure integrations with Snowflake, Databricks, APIs, and CRM systems; explain healthcare claims data and AI capabilities to diverse customers. Build demos, reusable enablement resources, and scalable architecture patterns while partnering with commercial, product, data, and engineering teams.

Build the core AI platform for an enterprise workspace that turns organizational data into memory, proactive agents, and customer workflows. Own reusable primitives such as triggers, tools, approvals, memory, observability, and workflow execution. Ship full-stack product and backend systems, work directly with customers, and contribute to roadmap, architecture, engineering practices, and company culture.

Build end-to-end frontend and backend features for a voice AI trust platform while working directly with enterprise customers. Responsibilities include customer discovery, debugging, proofs of concept, product decisions, reusable platform development, and post-sales technical support. The role requires TypeScript and Python proficiency, AI coding tool fluency, strong product judgment, clear communication, and approximately 10%–20% travel.

Build and operate Sysdig’s AI platform, including MCP servers, agent infrastructure, AWS Bedrock runtime and gateway, usage tracking, governance, observability, security controls, and employee AI automations. The role requires production ownership, AWS platform engineering, full-stack development, LLM application experience, and strong autonomy in designing secure, scalable AI systems.

Design and build explainable and contestable AI capabilities for models and agents. Develop hallucination detection, continual-learning systems, and production AI features from research prototypes. Contribute across model development, inference, deployment, monitoring, evaluation, and performance optimization. Partner with product, design, and customer teams to deliver trustworthy AI workflows. The role requires strong Python and software engineering skills, modern AI systems experience, and the ability to bridge research and production engineering.

Design and productionize explainable and contestable AI capabilities for LLM, VLM, agent, and RAG systems. Develop hallucination mitigation, continual-learning, evaluation, inference, deployment, monitoring, and retrieval solutions. Translate research into production features, optimize model serving, and collaborate with product, design, and customer teams. The role requires strong Python and software engineering skills, research-to-engineering fluency, and experience evaluating complex AI tradeoffs.

Build SeekrFlow Edge Builder, a platform for packaging, deploying, and operating trustworthy AI solutions across edge, cloud, on-premises, disconnected, and air-gapped environments. Responsibilities include developing orchestration, lifecycle management, model serving, optimization, security, observability, testing, APIs, diagnostics, and recovery capabilities. The role requires collaboration across AI, platform, infrastructure, security, product, and customer engineering teams, plus technical documentation and troubleshooting across complex deployment environments.

Lead firm-wide adoption and operationalization of AI/LLM-enabled software engineering across the SDLC/PDLC. Design and scale agentic systems, orchestration, guardrails, monitoring, and secure integrations. Partner with platform and App Dev leaders to deliver production-grade, auditable AI workflows that reduce developer toil, improve reliability, and meet governance, security, and observability standards.

Lead full‑stack development of scalable, secure services and UI using Java, Python, and React. Build AI/agentic capabilities with Smart SDK, design data/search solutions (PostgreSQL, OpenSearch), drive cloud-native engineering on AWS with Terraform, improve SDLC via AI-assisted tooling, mentor engineers, and deliver operational reliability.

Lead design and delivery of backend systems that embed AI-native capabilities (LLM features, agentic workflows). Build secure, high-quality production Java microservices, optimize performance, drive AI-assisted engineering practices, participate in architecture reviews, mentor juniors, and ensure operational stability and testing/validation standards.

Lead technical contributor on an agile Investment Banking engineering team. Design, develop, debug, and optimize secure production Java/J2EE/Spring Boot systems; drive adoption and validation of enterprise-approved AI-assisted engineering practices; lead vendor evaluations, automation and remediation efforts, and communities of practice; mentor engineers on responsible AI use and ensure test automation, performance tuning, and operational stability.

Lead development of AI‑first analytics and data products, modernize the data mesh, build autonomous human‑in‑the‑loop agents for schema drift and governance, design scalable batch and real‑time pipelines, enforce lineage/DQ/governance, and provide technical leadership across analytics, platform, risk, and domain teams.

Lead design and delivery of agentic and generative AI systems for banking: translate business problems into research and engineering plans, build multi-agent solutions, design reusable services and evaluation practices, mentor researchers/engineers, and partner to scale secure, reliable production deployments.

Develop, train, optimize, and deploy LLMs and multi-agent AI systems for tactical edge hardware. Interface agents with sensors, effectors, and drones, benchmark performance, and build reliable fail-safe production systems for defense customers.

Lead and execute large-scale spacecraft assembly, integration, and test campaigns. Define AI&T strategies, oversee cleanroom/lab integration, mentor engineers and technicians, author proposals, estimate cost/schedule, optimize test processes, and ensure operational readiness and quality for flight hardware.

Design and deliver LLM-powered, agentic AI systems to automate customer support and compliance. Build scalable, secure backend infrastructure in Python and Go, integrate third-party models/APIs, implement grounding and guardrails, and measure performance. Lead end-to-end projects, mentor engineers, conduct code reviews, and participate in on-call and incident response to improve resolution times and reduce operational costs.

Own and evolve Sonar's AI-native internal platform: design, build, and operationalize agent/connector integrations (Claude/MCP, Rovo), administer and productize Jira Service Management and Atlassian Cloud, implement scalable automations and integrations, maintain CMDB/assets, enforce ITSM/ITIL processes, support security/compliance audits, and partner across teams to drive platform roadmap and stakeholder enablement.

Develop end-to-end AI-powered applications, including React/Next.js interfaces, Python or Node.js backend services, APIs, and agentic workflows. Integrate LLM APIs and real-time streaming experiences, design scalable systems, write comprehensive tests, participate in code reviews and on-call support, and contribute to architecture. Collaborate with designers, product managers, and ML engineers to deliver customer-facing products.

Lead design and implementation of LLM-enabled analytics on Snowflake, build natural-language-to-SQL and conversational interfaces, develop semantic data models and prototypes, productionize AI solutions, set architectural best practices, mentor teams, and ensure responsible, governed AI usage.

Lead design and delivery of production-ready AI systems within the ION Factory OS, ship AI-driven and core product features, architect scalable cloud-native systems and ML pipelines, implement RAG and MCP integrations, collaborate with product and data teams, ensure reliability and scalability, and mentor junior engineers.