Top AI Engineer Jobs

Design, secure, and scale GCP cloud infrastructure supporting event-driven services, AI models, and automated releases. Maintain infrastructure as code and CI/CD automation, manage access controls and encryption, remediate penetration-test and vulnerability findings, and own monitoring, dashboards, on-call response, incident management, and performance improvements.

Build practical prototypes for AI verification, including distinguishing GPU training from inference workloads and exploring zero-knowledge proofs and privacy-preserving evaluations. The role spans ML engineering, inference pipelines, algorithms, frontend demonstrations, cybersecurity, and collaboration with external experts. It also involves technical communications and engagement with policymakers and international partners in an ambiguous, early-stage environment.

Build and deploy production AI agents for internal teams, including custom tools, function calling, execution harnesses, cloud services, and integrations. Partner with non-technical teams to identify automation opportunities, engineer LLM context, maintain tool libraries, and operate agents with monitoring, error handling, cost controls, prompt optimization, caching, and model selection.

Lead the development of Samba’s AI-native go-to-market infrastructure. Build multi-agent systems, MCP servers, RAG pipelines, evaluation frameworks, and Claude integrations across Salesforce, Gong, Slack, Snowflake, and advertising data. Create deal intelligence, seller automation, governed AI workflows, and enablement standards while reducing manual revenue operations work.

Designs, develops, and deploys end-to-end AI-powered healthcare applications across front-end, back-end, model integration, and data pipelines. Responsibilities include integrating LLMs and multimodal AI tools, developing RAG systems and AI agents, optimizing prompts and model costs, fine-tuning GPT and Llama models, evaluating AI performance, and documenting solutions. The role collaborates with technical, DevOps, product, and external stakeholders to deliver scalable enterprise software.

Own Brain Co.’s developer platform, including development environments, monorepo tooling, build systems, CI/CD, code-review automation, and engineering workflows. Measure and reduce developer friction through instrumentation and user feedback. Improve reliability, onboarding, adoption, and delivery speed across cloud, regulated, and air-gapped environments. Integrate AI-assisted development tools, support migrations, and operate the platform through its full lifecycle.

Leads the architecture, development, deployment, and operation of production AI, distributed systems, cloud infrastructure, automation, and data platforms. Integrates AI/ML models and LLMs into healthcare workflows, builds Databricks pipelines, governs RPA and ETL reliability, and establishes CI/CD, monitoring, security, and responsible AI standards. Mentors engineers, leads technical design and incident reviews, and partners across AI, data, product, clinical operations, and application engineering teams.

Lead engineering teams and technology portfolios while designing and implementing large-scale, distributed, cloud-native systems. Build full-stack solutions using modern programming languages, microservices, databases, containers, and cloud platforms. Provide technical leadership on architecture, platform roadmaps, reliability, observability, coding standards, and enterprise technology strategy. Mentor engineers, collaborate with product and architecture leaders, and drive cross-functional technical initiatives supporting regulatory and customer needs.

Develop and deploy physical AI systems on real robots, including learned perception, vision-language-action models, imitation and reinforcement learning, world models, autonomous behavior, and sensor-data pipelines. Prototype approaches in simulation and field environments, optimize models for latency and edge deployment, and collaborate across robotics, controls, embedded, mechanical, and product engineering teams. The role also contributes to long-term Physical AI architecture and research direction.

Build AI-driven decision and optimization systems that enforce feasibility, authority, policy, safety, timing, compatibility, and asset constraints. Develop deterministic constraint frameworks, validate operational data, integrate checks into real-time and batch planning pipelines, and provide explainable diagnostics for infeasible plans. Partner with routing, optimization, AI-agent, and execution teams to ensure logistics actions are auditable, policy-compliant, and operationally realistic in high-stakes defense and disaster-response environments.

Build and deploy production-ready generative AI applications across rotating federal engagements. Responsibilities include developing RAG pipelines, conversational AI, and multi-agent systems; integrating LLMs and cloud services; implementing observability, security, cost controls, and responsible AI guardrails; troubleshooting integrations; training client teams; documenting solutions; and contributing reusable patterns to an AI solution catalog.

Develops AI-enabled applications for federal cybersecurity audit readiness, compliance analysis, governance, and risk management. Builds GPT, RAG, agentic workflow, vector database, data pipeline, dashboard, and predictive analytics solutions using audit and compliance data. Implements secure AI governance, model evaluation, prompt security, logging, explainability, and NIST-aligned controls. Collaborates with senior government stakeholders to translate cybersecurity requirements into proof-of-concept and production capabilities.

Develop, optimize, and deploy production-grade computer vision and deep learning models for real-time perception and navigation on edge hardware. Integrate AI systems with robots, drones, unmanned systems, and swarms while optimizing inference with CUDA and TensorRT. Manage containerized deployments using Docker and Kubernetes, contribute to CI/CD and automated testing, and transition research prototypes into reliable production features.

Design, develop, and deploy production-grade AI applications using LLMs, foundation models, RAG architectures, vector databases, and agentic workflows. Build REST APIs, backend services, and cloud-native systems while integrating APIs, external tools, and enterprise knowledge sources. Collaborate with software engineers, product managers, and AI specialists to translate business requirements into scalable solutions. Apply software engineering best practices, with opportunities to work on multi-agent systems, observability, containerization, and frontend technologies.

Build and operate production infrastructure for entity-focused AI agents. Responsibilities include scheduled benchmarking and golden gates, scalable entity-agent pipelines, evidence adapters, replayable vendor stores, agent-platform deployment, review and batch tooling, rollback mechanisms, and comprehensive cost, quality, latency, and health instrumentation. The role partners with AI engineering, product, research, data, and platform teams to move prototypes into reliable, self-service production systems.

Leads AI and machine learning strategy, projects, and teams across the full model lifecycle. Responsibilities include developing and deploying models, establishing governance and MLOps standards, evaluating emerging technologies, managing AI risks, mentoring technical staff, partnering with stakeholders, and reporting business impact and delivery progress to senior leadership.

Design, build, deploy, and optimize production AI systems, including LLM integrations, RAG pipelines, embeddings, vector search, predictive models, and inference systems. Collaborate with product and engineering teams to translate business needs into scalable solutions for recommendations, risk scoring, forecasting, and workflow automation. Own data pipelines, monitoring, evaluation, security, privacy, documentation, and continuous model improvement across cloud environments.

Supports organization-wide IT infrastructure while integrating, deploying, monitoring, and troubleshooting AI applications and machine learning models. Maintains system performance, capacity, security, firewall compliance, and cloud environments. Provides technical support, resolves incidents, modifies software packages, contributes to system design and testing, and serves as a subject-matter expert and escalation point for junior engineers. Requires familiarity with RAG, cloud architecture, virtualization, enterprise security, and evolving regulatory requirements.

Develops and supports enterprise AI applications, data pipelines, software integrations, and full-stack prototypes. Designs data structures, ETL processes, queries, reporting solutions, and data warehousing strategies. Troubleshoots production AI incidents, performs systems analysis and testing, supports project planning, maintains applications, and provides technical guidance to junior staff. Applies RAG, vector databases, agentic AI, and Model Context Protocol concepts while supporting financial crime and compliance-related systems.

Build and maintain Nexus, an enterprise AI implementation platform. Develop backend services, APIs, integrations, orchestration workflows, automated data pipelines, transformations, semantic models, validation processes, and governed lakehouse assets. Partner with AI engineers and enterprise customers to deploy solutions, resolve complex data and system requirements, and create reusable integrations. Ensure secure, monitored, recoverable, and repeatable deployments across customer environments.

Own the design and implementation of Azure platform infrastructure for secure, repeatable enterprise AI deployments. Build IaC modules, identity and security controls, networking, CI/CD automation, observability, reliability tooling, and customer environment automation. Partner with product engineers and enterprise teams to standardize deployments across complex Azure environments, including decisions involving containers, scaling, resilience, isolation, and cost.

Deploy Nexus enterprise AI software for customers, from technical discovery through production validation and handoff. Build integrations, Python and SQL transformations, notebooks, APIs, and automation; validate data pipelines and semantic models; resolve technical issues; document and stabilize deployments; communicate with customer stakeholders; and convert recurring deployment challenges into reusable engineering improvements.

Evaluate and improve generative AI model responses for quality, safety, accuracy, and performance. Assist with debugging, optimization, data preparation, annotation, validation, and development of secure, scalable GenAI applications using AWS Bedrock and Anthropic Claude. Review and write Python and ReactJS code, maintain technical documentation, track tasks, attend meetings, and conduct research.

Leads the design, development, deployment, and maintenance of production AI/ML solutions in classified AWS environments. Builds RAG and agentic AI systems, provides architectural guidance, supports secure system integration, and communicates technical findings to technical and nontechnical stakeholders. The role also includes data architecture, model optimization, containerized application development, DevSecOps practices, and technical leadership for AI/ML engineering teams.

Build and scale LLM-based, agentic customer-engagement systems: architect, deploy, and monitor production ML features; finetune models; create evaluation frameworks, feedback loops and HITL workflows; run A/B tests; optimize latency, cost, and reliability; and mentor the team.