Top AI Engineer Jobs

Lead design, prototyping, and operationalization of AI/ML solutions for intelligence mission workflows. Develop LLM integrations, scalable architectures, and analytics to automate discovery, accelerate insights, and transition capabilities to production while shaping technical strategy and best practices.

Senior individual contributor leading AI adoption in firmware engineering: evaluate AI tech, prototype and scale LLM/agent systems, build AI-assisted CI/CD and IDE tooling, architect secure AI infrastructure, and drive upskilling and cross-functional partnerships to improve firmware quality and velocity.

Lead design, build, and scaling of Agentic AI and industry-specific solution accelerators on Genesys Cloud. Develop reusable multi-agent workflows, RAG-based knowledge orchestration, integrations with enterprise systems, prototypes, and production-grade AI applications. Provide technical leadership, mentor engineers, collaborate with product and industry SMEs, and deliver repeatable solution blueprints to accelerate deployment and time-to-value.

Lead and grow an AI engineering organization to design, build, and operate production-grade AI services. Define architecture, drive execution, implement RAG, agents, and orchestration, ensure reliability, observability, and CI/CD, and partner with Product, Data, Security, and business stakeholders while recruiting and mentoring engineers.

Own and architect the frontend foundation for Handshake AI Enterprise: establish component system, design tokens, engineering patterns, shared libraries, and ship production-grade UIs while collaborating with AI and platform teams to ensure scalable, maintainable, high-performance enterprise deployments.

Design, build, and operationalize scalable AI/ML infrastructure and data pipelines. Implement model lifecycle management (MCP), integrate agent frameworks (e.g., LangChain), manage vector DBs and feature stores, enable HITL workflows, maintain data architectures, mentor junior engineers, and present work internally and externally.

The Software Engineer AI will design, develop, and deploy AI-powered product features in a cloud-native environment, ensuring technical excellence and collaboration across teams to enhance the candidate experience.

Lead cross-functional engineering workstreams to design software, integration, data, testing, and infrastructure architectures. Define and encode engineering best practices into AI coding agents, design AI-assisted workflows, select AI tools, implement CI/CD, automated and chaos testing, and lead performance, integration, and data strategies. Provide technical leadership, mentorship, and measurable quality reporting at enterprise scale.

Design, implement, and operate large-scale Ads infrastructure on Azure. Build and optimize data ingestion, storage, and streaming pipelines; ship new ad-product features; apply ML/AI to improve ad quality; analyze data for technical design; and handle live-site escalations with customers.

Leads enterprise data ingestion and integration for AI, machine learning, Generative AI, analytics, and BI workloads. Designs scalable pipelines for structured, semi-structured, unstructured, and multimodal data; establishes quality, governance, lineage, security, and observability standards; and supports RAG, AI Search, vector retrieval, and MCP capabilities. Partners with technical and business stakeholders, mentors engineers, evaluates emerging technologies, and drives production delivery and continuous improvement in a regulated financial-services environment.

Lead design and implementation of browser-based security, identity, and management integrations for enterprise Microsoft Edge. Drive technical strategy and cross-product initiatives, mentor engineers, and use telemetry and customer feedback to improve AI-powered productivity and security experiences.

Architects, develops, and deploys secure AI solutions for federal and defense organizations. Responsibilities include building RAG and LLM-agent systems, semantic search, NLP applications, predictive analytics, automated data pipelines, chatbots, explainable AI architectures, and interactive dashboards. The role integrates structured and unstructured enterprise data while ensuring compliance with DoD security requirements across classification levels.

Lead development of AI workforce platform infrastructure, including deployment automation, CI/CD pipelines, observability, configuration standards, prompt and skill marketplaces, MCP integrations, and workflow playbooks. Partner with engineering teams to embed AI tooling into development workflows, improve platform capabilities, document best practices, and provide technical leadership. The role includes production-quality coding, cross-functional communication, on-call participation, and business-hours customer support.

Conduct offensive red-team assessments of AI-enabled systems, including models, hardware, software, and networks, supporting national security missions. Develop attack tactics, techniques, procedures, and operational tooling in Python, PowerShell, C, and Bash. Perform penetration testing, exploit development, reverse engineering, vulnerability analysis, and Windows/Linux security assessments. Communicate findings to mission partners, mentor colleagues, and travel to field sites. The role requires eligibility for a Department of War security clearance.

Conduct red-team assessments of AI-enabled systems, including models, hardware, software, and networks, supporting national security missions. Develop offensive tactics, techniques, procedures, and operational tools using Python, C, Bash, and PowerShell. Perform penetration testing, exploit development, reverse engineering, vulnerability analysis, and Windows/Linux security assessments. Communicate findings to mission partners, travel to field sites, and mentor colleagues.

Designs, builds, and integrates AI/ML solutions and microservices for defense and intelligence missions. Responsibilities include fine-tuning language models, developing RAG pipelines and vector search integrations, designing model training and data pipelines, deploying containerized services with Docker and Kubernetes, integrating security scanning into CI/CD, managing multi-cloud environments, and mentoring developers in Agile teams.

Leads architecture and implementation of enterprise AI applications and agentic LLM workflows. Optimizes model inference, memory usage, quantization, and deployment for air-gapped environments. Designs multi-cloud Kubernetes infrastructure with Terraform, establishes DevSecOps security scanning and remediation, and drives Agile execution, technical debt management, and team mentorship.

Supports the development, testing, and deployment of AI and machine learning pipelines in secure environments. Responsibilities include maintaining ML data ingestion workflows, writing Python and Bash code, containerizing applications with Docker, conducting unit tests and security scans, and participating in Agile ceremonies, code reviews, and backlog refinement.

Architects and leads enterprise-scale AI software systems, including novel AI frameworks, LLMs, MLOps, multi-cloud and air-gapped infrastructure. Serves as the technical authority for high-assurance machine learning, container security, DevSecOps, and enterprise architecture. Advises government and agency leadership, establishes security standards, leads vulnerability remediation, and aligns engineering roadmaps with mission objectives.

Rapidly prototype, evaluate, and productionize AI-native product features (LLM agents, natural-language interfaces, human-in-the-loop flows, RAG). Build secure, reliable data pipelines, observability, and scalable architectures; run experiments, measurement harnesses, and work directly with customers and cross-functional teams to ship high-impact features.

Designs, builds, and operates AI-powered solutions for go-to-market teams, including retrieval, context, evaluation, guardrails, agent workflows, and operational automation. Owns problems end to end from discovery through launch, measurement, and iteration while shaping data and system architecture. Establishes reusable AI tooling, improves engineering velocity through AI-augmented development, mentors engineers, and maintains high standards for reliability, privacy, security, observability, and customer trust.

Develop and deploy AI and machine learning solutions using LLMs, RAG, NLP, search, classification, personalization, and information retrieval. Build end-to-end model pipelines, operationalize orchestration, analyze large datasets, and monitor deployed model performance. Collaborate with business, product, project management, and technology teams to define AI initiatives, roadmaps, and milestones while delivering scalable, maintainable, and well-tested software.

Part-time AI Application Engineer focused on measuring and improving healthcare AI agent performance in production. Responsibilities include instrumenting agent quality, identifying failure points, designing experiments, analyzing results, and partnering with clinical experts to improve patient outcomes. The role requires Python, strong analytical judgment, comfort with ambiguity, and interest in healthcare. Clinical experience is valued but not required. Engagement is structured as a paid internship or contract role with weekly Bay Area office syncs.

Lead architecture and development of an enterprise agentic AI and MLOps platform. Build scalable backend services, SDKs, autonomous and multi-agent systems, evaluation and observability pipelines, memory and state infrastructure, and RAG capabilities. Establish engineering standards, improve developer velocity, collaborate with product and engineering teams, and mentor junior engineers. The role requires deep distributed-systems, Kubernetes, cloud, LLM orchestration, vector database, and agentic framework expertise.

Leads Gen AI software engineering and enterprise architecture efforts, driving design and code reviews, architectural standards, design patterns, and long-term technology strategy. Guides large-scale development projects, evaluates technical risks, supports cloud and microservices solutions, and partners with business and technology stakeholders. Mentors engineers, promotes CI/CD and quality improvements, resolves delivery blockers, communicates metrics, and ensures compliance with applicable policies and regulations.