Top AI Engineer Jobs

The Senior Software Engineer will build AI products, create user-centric interfaces, and collaborate across teams to drive product growth through intuitive design and full-stack development.

Develop intuitive AI applications, collaborate with teams, lead software development, and drive product growth in a 0-to-1 environment.

Lead the design and development of ML inference systems, focusing on generative AI models and optimization techniques for production environments.

Design and implement AI solutions for Marine Corps needs, develop algorithms, mentor team members, and create scalable data platforms. Requires collaboration in a team environment.

Develop a unified AI platform by designing and building scalable applications, improving platform performance, and collaborating with ML engineers.

The Automation Engineer will design, maintain automation equipment, support sites, develop solutions, and ensure timely delivery of automation requirements.

Deploy, optimize, and integrate LLMs and multimodal models on local GPU/ARM64 hardware. Develop custom CUDA kernels, tune inference (TTFT, tokens/sec), connect backends to orchestration layers, build prototypes and frontends, and implement device communication protocols for local AI compute.

Define architecture and technical roadmap for production AI/ML systems, productionize NLP and voice features in Python, mentor engineers, partner with product/medical teams to implement responsible safeguards, build evaluation and monitoring frameworks, own AWS Bedrock strategy, and drive long-term AI strategy and improvements across Fabric's products.

The Data Engineer will develop and maintain AI context data capabilities, create data pipelines, ensure operational excellence, and follow compliance protocols. Responsibilities include data ingestion, retrieval quality improvements, and semantic layer implementation for AI integration within healthcare organizations.

The role involves building and maintaining AI data capabilities, implementing data pipelines, and ensuring operational excellence in data management within a healthcare context.

Lead design, build, and deploy scalable Generative AI and ML systems into production. Implement LLM integration, prompt engineering, RAG, conversational interfaces, model monitoring, evaluation frameworks, APIs, inference optimization, and collaborate to productionize forecasting and ML models.

Design, build, and deploy production-grade generative AI features (RAG, embeddings, vector search, agents) for healthcare applications. Implement backend APIs, integrate AI into front-end interfaces, ensure HIPAA-compliant data handling, and establish evaluation, monitoring, and guardrails for safe, reliable AI in clinical and member-facing systems.

As a Tier 3 Support Engineer, you will resolve complex technical issues, work with engineering teams to enhance products, and document solutions.

Design, build, and scale MLOps infrastructure and CI/CD/continuous training pipelines. Deploy and monitor ML models as microservices or batch jobs, manage cloud GPU/CPU resources with IaC, enable observability for model drift and latency, and collaborate with data scientists, engineers, and product teams to ensure reproducibility, compliance, and production reliability.

Build and operate AI-native backend services that orchestrate multi-step, stateful LLM agents for GTM workflows. Design model-agnostic abstractions, integrate external systems, deploy containerized services on Kubernetes, ensure observability, and run experiments to measure effectiveness.

Design and operate identity and access for AI agents, build governance and review processes for connectors, maintain centralized agent registry and telemetry, control AI license/cost governance, automate IT operations, and collaborate across IT, Engineering, Security, and Finance.

Build and ship production agents and reusable skills on a petabyte-scale observability platform. Own end-to-end agent stack (context engineering, tooling, MCP servers, SDKs, integrations), improve agent observability, evaluate models, and optimize for latency, cost, and reliability while collaborating openly with OSS contributors and customers.

Design, build, and operate GSK's GenAI platform components and production code (primarily Python). Enable LLM training, multi-agent applications, reproducible deployments, and integration with DataOps and Data Engineering. Drive API/SDK design, CI/CD best practices, scalability, observability, and cross-team collaboration to deliver performant GenAI services for R&D teams.

Build and productionize internal AI-powered tools and data systems. Implement and maintain infrastructure, data pipelines, deployments, monitoring, and access controls. Integrate third-party AI services, ensure data quality, document systems, and collaborate with stakeholders to deliver maintainable solutions.

Lead design and implementation of high-performance agent runtimes, backend services, and self-hosted model/agent infrastructure. Build low-latency execution systems, real-time data pipelines, telemetry loops, CI/CD for zero-downtime deployments, and ensure fleet reliability on AWS/EKS.

Design, build, and maintain scalable, secure AI core platform and web/data pipelines. Drive observability, auth/network integrations, runtime scalability, AI/ML infra for LLMs, mentor engineers, partner across teams, and ensure PHI security and compliance.

Design, implement, and scale web and data pipeline solutions focused on generative AI and LLMs. Improve AI/ML infrastructure for model development, training, deployment, and prompt engineering. Partner with product and stakeholders to prioritize applied AI use cases, ensure PHI security and regulatory compliance, mentor junior engineers, and contribute APIs and integrations to embed generative AI into healthcare applications.

Embed with product/tech cohorts to build and ship Claude Code plugins, skills, hooks, and MCP integrations; generalize work into reusable platform artifacts; integrate services via the MCP Gateway and AWS; support adoption, documentation, and on-call for marketplace artifacts while partnering on security, auth, and observability.

Embed with product/tech cohorts to build plugins, skills, hooks, and connectors; integrate tools via MCP Gateway and AWS; productize reusable patterns to the plugin marketplace; mentor engineers, measure adoption, and support on-call for published artifacts.

Own and grow OUTFRONT's internal AI platform and external agent infrastructure: move OUTGPT to production, evolve the Agency Connect MCP, design agent infrastructure (routing, guardrails, observability, secrets, cost controls), operationalize AWS AI stack, integrate with internal systems, and mentor the AI & Automation engineering pod.