Top AI Engineer Jobs

Build and deploy AI capabilities that improve internal workflows and user experiences. The role involves discovering high-impact automation opportunities, shipping AI applications with robust evaluation practices, partnering on data and architecture, and creating reusable patterns. Over time, the engineer will develop physician- and employer-facing intelligence features. Success requires strong end-to-end software engineering, product ownership, stakeholder discovery, and experience delivering LLM-powered applications to production.

Lead technical program execution for AI/ML and secure cloud initiatives across intelligence community programs. Coordinate stakeholders, prioritize backlogs, manage timelines, and oversee deliverables. Drive RMF and ATO accreditation activities, including security documentation and coordination with ISSOs and ISSMs. Support secure workspace development and assist AI/ML developers with Python, testing, documentation, architecture, and proof-of-concept solutions.

Leads rapid design, prototyping, deployment, and operationalization of AI/ML proof-of-concepts for Intelligence Community missions. Builds GenAI, NLP, computer vision, and predictive analytics solutions; integrates MLOps pipelines into secure cloud and air-gapped environments; coordinates delivery across multiple programs; and presents demonstrations to senior stakeholders. Requires advanced software engineering, AI/ML frameworks, secure deployment experience, and an active Top Secret clearance with eligibility for TS/SCI.

Designs, develops, tests, troubleshoots, and maintains secure, scalable software solutions using Java, with exposure to React and AWS. Creates architecture and design artifacts, supports application resiliency and CI/CD, analyzes data to improve systems, and uses enterprise-authorized AI-assisted development tools while validating outputs for correctness, performance, and security.

Develop and evaluate machine learning algorithms for satellite recognition, tracking, sensor scheduling, anomaly detection, prediction, classification, clustering, and optimization. Perform exploratory data analysis, build scalable HPC pipelines, conduct space-related engineering analyses, validate external algorithms, collaborate with satellite and astrodynamics experts, and present technical findings to customers and stakeholders.

Develop and implement AI/ML algorithms across computer vision, natural language processing, time series, graph data, reinforcement learning, and large language models. Analyze large datasets, apply research publications, document technical solutions, and understand AWS-based training and deployment pipelines. Mentor team members and collaborate within interdisciplinary research teams.

Found the company’s Generative AI capability, architecting and shipping the end-to-end GenAI platform, including data and context layers, model serving, agent frameworks, evaluation, guardrails, and observability. Leads a hands-on team of GenAI engineers, MLOps engineers, and applied scientists while deploying agentic systems across vehicle engineering, manufacturing, supply chain, software, and go-to-market functions. Builds proprietary AI infrastructure and partners with manufacturing and quality teams to apply AI in physical-world operations.

Develop and deploy AI- and data-driven applications using Palantir AIP, Ontology, models, workflows, and custom widgets. Translate ambiguous mission requirements into prototypes, MVPs, and scalable production solutions while collaborating with users, analysts, product owners, engineers, and stakeholders. Integrate datasets, pipelines, APIs, models, and external systems; troubleshoot solutions; document architecture; conduct testing and demonstrations; and support deployment. The role requires an active TS/SCI clearance.

Design, build, deploy, and operate production-grade AI and machine learning systems using Java, Python, and GCP. Develop scalable APIs, backend services, data pipelines, model-serving systems, and cloud-native platforms. Implement model lifecycle management, monitoring, testing, observability, and optimization. Collaborate with product managers, data scientists, engineers, and stakeholders to deliver secure, reliable AI solutions. Mentor engineers, participate in architecture and code reviews, and evaluate emerging AI technologies.

Leads AI-enabled manufacturing optimization across production facilities by analyzing machine, downtime, scrap, and production data. Develops monitoring dashboards, alerts, predictive maintenance tools, troubleshooting guidance, and operational KPIs. Partners with operations, maintenance, engineering, and leadership teams to improve OEE, throughput, uptime, quality, and efficiency. Supports IoT data collection, machine analytics, process improvements, and implementation across facilities, with travel as needed.

Design, build, and deploy production-grade agentic AI systems, including autonomous and multi-agent architectures, tool-use orchestration, memory, retrieval, and function-calling workflows. Integrate agents with enterprise tools and databases, implement evaluations, guardrails, observability, fallback paths, and human-in-the-loop controls. Lead technical strategy, client collaboration, prototyping, and product direction for enterprise AI initiatives.

Build and lead production AI infrastructure for institutional commercial real estate. Develop full-stack workflows, document parsers, data foundations, MCP servers, LLM features, evaluation systems, and React-based portals. Deploy solutions in client Azure environments, productize reusable components, ensure auditable accuracy, work directly with clients, and help hire and lead distributed engineers.

Refine autonomous AI-agent workflows for MGA BDX data profiling. Analyze insurance data to identify quality issues, anomalies, missing fields, duplicates, structural variations, and business-rule exceptions. Define profiling plans, validation logic, metrics, and review processes; improve prompts, agent behavior, and human-in-the-loop controls. Collaborate with data consumers, onboarding teams, engineers, and enterprise stakeholders to produce clear, traceable outputs for BDX platform and data hub onboarding.

Own end-to-end production data platforms, writing Python and SQL, designing AWS pipelines, modeling analytics data, and implementing quality and reconciliation controls. Make architecture decisions independently, ensure correctness and auditability, and use AI coding tools effectively. The role also supports applied AI initiatives including entity resolution, LLM-assisted analytics, embeddings, and anomaly scoring.

Lead the architecture, implementation, and operation of scalable AI/ML infrastructure. Build CI/CD pipelines for model development and deployment, manage data ingestion, serving, monitoring, security, and performance, and collaborate with AI researchers and software engineers. Mentor junior engineers and establish best practices for AI/ML platform development, automation, and cloud infrastructure.

Apply FANUC robotics software to physical AI, external motion control, virtual commissioning, and digital twin applications. Provide pre- and post-sales technical support, simulations, demonstrations, troubleshooting, and application optimization. Investigate issues, develop proof-of-concept solutions, support product planning and validation, and create demonstration systems. The role also conducts market analysis and contributes technical requirements for intelligent robotics products and software enhancements.

Build and ship production AI-powered development features, including agentic workflows, LLM pipelines, retrieval systems, evaluations, fine-tuning, and latency optimization. Own AI feature quality from prototyping through deployment and monitoring. Collaborate with Product, Infrastructure, and Research teams to scale AI capabilities for Bubble’s visual app-building platform.

Lead the design, training, fine-tuning, deployment, and monitoring of generative AI, large language model, retrieval-augmented generation, and multimodal systems. Build scalable cloud-based MLOps pipelines, optimize inference, conduct model evaluation and bias assessments, and support responsible AI, security, compliance, and audit readiness. Collaborate with data scientists, platform engineers, and business stakeholders while mentoring junior engineers and translating enterprise use cases into secure operational AI solutions.

Design, build, and deploy production-grade LLM agents, RAG systems, voice/image AI, and automation workflows. Implement TTS/STT and generative image models, develop full-stack APIs/microservices, optimize performance, and ensure security/compliance while collaborating with product and operations teams.

Design, build, and maintain batch and streaming data pipelines on GCP (BigQuery, dbt, Airflow, Pub/Sub). Productionize ML workflows (training, inference, CI/CD) using Cloud Run, GKE, and Vertex AI. Support real-time inference, RAG/LLM assistants, graph-based recommendations, and model monitoring. Collaborate with cross-functional teams to deliver scalable, reliable data products and observability for downstream ML and analytics.

Build, deploy, monitor, and support production AI tools, agents, workflows, backend services, and interfaces. Integrate AI systems with Salesforce and Microsoft 365 while applying secure authentication, permissions, and audit practices. Develop retrieval and extraction pipelines, prompt and tool designs, validation logic, and documentation. Collaborate with stakeholders to turn ambiguous needs into usable solutions, support internal users, conduct enablement sessions, and continuously improve AI tooling under senior engineering guidance.

Lead design and build of a global AI/ML deployment platform: architect control plane, multi-region deployments, APIs, autoscaling, and integrations. Own technical direction, engage clients, drive governance of AI-assisted development, and ensure resilient, secure production-grade infrastructure.

Designs and implements AI tools, agent services, developer infrastructure, and workflows using locally hosted models. Integrates AI with code repositories, documentation, build systems, and issue trackers; develops datasets, fine-tuning workflows, evaluation practices, and model governance. Optimizes inference performance and reliability, collaborates with infrastructure and security teams, documents best practices, and mentors software teams on responsible AI adoption.

Build and deploy production machine learning systems, including LLMs, agents, and RAG applications, for healthcare, government, energy, and other regulated industries. Responsibilities include developing scalable data pipelines, optimizing model performance, monitoring deployed models, translating research into practical solutions, collaborating with leaders and cross-functional teams, and contributing to software engineering practices. The role is designed for a 2027 bachelor's graduate with hands-on generative AI and machine learning framework experience.

Build backend services, data pipelines, APIs, distributed systems, developer tools, and shared platform capabilities for AI products. Develop ML experiment tracking, artifact management, and automated training and evaluation pipelines. Improve reliability, latency, and throughput while collaborating with Product and ML teams. Own scoped projects involving implementation, testing, maintenance, and clean API design for enterprise and government customers.