Top AI Engineer Jobs

Develop and maintain complex software systems, including AI automation workflows, integrations, algorithms, and high-volume data-processing solutions. Build CI/CD pipelines, debug and enhance applications, develop database queries, implement monitoring, optimize performance, and execute software testing. The role also involves requirements analysis, documentation, system installation, quality control, and collaboration with engineering teams. Work is performed near Fort Meade, Maryland, and requires TS/SCI with a polygraph.

Build and own AI-powered internal tools from discovery through deployment, rollout, and ongoing improvements. Develop document analysis, agentic workflows, automation, research, routing, and analytics systems that reduce manual work. Partner with teams across the company to identify opportunities, scope solutions, enable employees to use AI effectively, and support customer-facing analytics and implementations.

Build and optimize production systems for heterogeneous machine learning models, including small models, medium models, and LLMs. Develop distributed systems for large-scale data processing, model training and validation, experimentation, deployment, and CI/CD. Ensure system robustness, scalability, and long-term maintainability while supporting data management and evolving model-development workflows.

Build and deploy AI agents, automations, and internal tools that improve productivity across go-to-market, finance, operations, product, and engineering. Identify high-impact automation opportunities, partner with functional and engineering leaders, contribute to developer productivity workflows, maintain internal knowledge systems, and drive adoption. The role requires hands-on technical ability, process judgment, commercial awareness, and ownership from opportunity identification through implementation.

Build and deploy autonomous AI agents and LLM-powered applications for insurance brokerage workflows. Responsibilities include owning the development lifecycle, creating evaluation frameworks and benchmarks, monitoring production performance, optimizing and scaling systems, mentoring teammates, and promoting AI adoption across the company. Requires production software engineering experience, LLM application expertise, dataset development, benchmark creation, and TypeScript proficiency.

Design, build, and deploy production AI systems that process large volumes of unstructured healthcare records. Translate customer problems into technical solutions, develop retrieval-based pipelines and multi-agent workflows, integrate healthcare data standards, and own monitoring and optimization. Partner with customers, leadership, product, and safety teams while identifying opportunities to expand AI adoption.

Build and operate production AI agents that complete property-management workflows across voice, SMS, web chat, and email. Integrate agents with APIs and systems of record, implement guardrails for payments and tenant data, manage stateful workflows and human escalation, and improve reliability through evaluations, tracing, regression testing, and cost and latency monitoring. Partner cross-functionally to turn operational processes into safe, measurable agent capabilities.

Build and maintain AI-powered healthcare products for patient engagement, care coordination, and claims management. Develop full-stack software, APIs, backend services, automation pipelines, and LLM integrations using Claude Code. Apply software engineering, DevOps, and MLOps practices for testing, deployment, monitoring, security, and performance. Collaborate remotely with product, design, engineering, and clinical stakeholders in an Agile environment.

Executes ETL pipelines, data ingestion, analytics production runs, and machine learning inference operations for a government supply chain program. Maintains pipeline automation using Python, SQL, and orchestration tools; monitors model performance, validates data quality, produces Red Team and Blue Team analytics, documents workflows, and supports configuration releases under technical oversight.

Build and maintain internal agentic AI platforms and developer-facing tools that integrate LLMs with engineering workflows. Partner with product and infra teams to design standards for agent deployment, reliability, evals, security, onboarding, and adoption measurement to improve developer productivity.

Lead design and delivery of full‑stack, cloud‑native AI and GenAI solutions; define AI strategies; architect secure, scalable platforms; mentor engineering teams; run pilots, integrate successful technologies, and partner across cloud, security, data, and business stakeholders to improve developer experience and drive enterprise adoption.

Lead end-to-end development of a greenfield enterprise platform for software dependency risk management. Build Java services, APIs, data ingestion pipelines, dependency analysis capabilities, and developer-facing features on AWS. Guide engineers and teams in secure AI-assisted development, code quality, testing, governance, and operational stability. Provide technical direction, influence architecture and product decisions, support production systems, and collaborate with product managers and designers to deliver scalable platform capabilities.

Evaluate AI coding-agent interactions for usefulness, correctness, engineering judgment, explanations, and trustworthiness. Provide clear written and video feedback, distinguish response quality levels, and help define standards for tools such as Codex, Claude Code, and Cursor. The role requires senior engineering experience, strong TypeScript/JavaScript or Python skills, familiarity with AI-assisted development, excellent English communication, and comfort making rigorous subjective judgments.

Lead the architecture and engineering execution of frontier AI prototypes and production integrations. Build and validate distributed AI systems, LLM applications, agentic and multi-agent systems, and physical AI pipelines across cloud, hybrid, edge, and disconnected environments. Evaluate emerging technologies, guide engineers and researchers, conduct design reviews, and translate research into scalable frameworks, reference architectures, and revenue-generating solutions.

Leads platform-wide performance, scalability, and resilience engineering across edge devices, in-store servers, and cloud services. Defines performance models, load-testing strategies, capacity plans, infrastructure sizing, resilience patterns, observability requirements, and customer-facing system behavior. Builds virtualized performance environments and uses AI-enabled validation to detect regressions, evaluate failure scenarios, and automate production-readiness testing.

Design, build, evaluate, and productionize machine learning and LLM-based agentic systems for healthcare products. Own projects end-to-end, including problem framing, dataset development, modeling, evaluation, deployment, monitoring, and safety guardrails. Build ML infrastructure, data pipelines, inference systems, and evaluation frameworks. Collaborate with clinicians, product managers, and engineers, while setting technical direction and mentoring team members.

Build and deploy production-grade AI systems for regulated institutions. Own ambiguous ML problems end-to-end, including data definition, model training, evaluation, deployment, and monitoring. Work with LLMs, vision models, reinforcement learning, agentic systems, and rule engines to process complex documents and support institutional decisions. Collaborate directly with customers, engineer for accuracy, latency, cost, and reliability, and develop rigorous evaluations and failure-mode taxonomies.

Own and evolve patient-facing clinical application surfaces, including a React widget, clinician portal, and Python service layer. Lead full-stack architecture, AI application integrations, encounter data flows, internal tooling, versioned shared components, and operational reliability. Partner with AI engineers on LLM-enabled workflows, maintain dependencies, protect consumers from breaking changes, and mentor junior engineers. The role requires durable ownership of rapidly built systems and delivery of critical healthcare technology.

Develop and maintain MDLinx software features, contribute to system design and code reviews, apply testing and software engineering practices, and troubleshoot production issues. The role emphasizes critically reviewing AI-generated code, identifying opportunities for AI-assisted automation, and supporting application performance, security, and compliance. Engineers collaborate across technical and business teams while expanding ownership and learning the pharmaceutical and media domains.

Leads enterprise-wide AI and engineering transformation as a distinguished technical individual contributor. Drives adoption of AI-powered product capabilities, agentic customer experiences, AI-assisted development, and modern engineering practices. Provides architectural leadership, evaluates emerging technologies, mentors teams, aligns senior stakeholders, and leads complex initiatives through influence across platform, product, AI/ML, and application engineering organizations.

Lead architecture and implementation for reliable, scalable AI authoring platforms. Build cloud infrastructure, CI/CD pipelines, Kubernetes and Helm deployments, infrastructure-as-code, observability, disaster recovery, A/B testing, and release processes. Improve backend reliability, performance, security, and cost efficiency across Azure and Google Cloud. Partner with product and engineering teams on technical strategy, ML infrastructure, LLM inference, and AI-assisted game development tools.

Build and deploy production-grade Generative AI and Agentic AI solutions for enterprise clients. Responsibilities include designing LLM applications, RAG systems, AI agents, and multi-agent workflows; integrating enterprise data and applications; deploying and scaling cloud-based systems; and optimizing reliability, security, performance, and cost. The role requires direct client engagement, strong software engineering skills, cloud expertise, and hands-on experience with LLM frameworks, agent orchestration, and production AI operations.

Develop and operationalize AI/ML capabilities for secure Department of Defense mission systems. Responsibilities include building agentic workflows, production APIs, MLOps pipelines, model validation and monitoring processes, and integrations with MSS Foundry and Databricks. The role requires production AI/ML deployment experience, Python proficiency, lifecycle management expertise, and collaboration with engineering and mission teams. Candidates must hold an active TS/SCI clearance and have at least five years of relevant experience.

Own the architecture and technical delivery of an internal AI orchestration platform. Build infrastructure, shared libraries, developer tools, agent frameworks, and secure automated execution environments that enable feature teams to integrate agentic AI. Optimize LLM interactions, prompts, token usage, embedding stores, testing, deployment pipelines, and platform architecture. Partner across UI, API, and data layers while mentoring teams and operating within an Agile environment.

Build and operate the platform supporting internal applications, dashboards, AI agents, data pipelines, and CI/CD. Manage Azure hosting, networking, identity, secrets, monitoring, access controls, infrastructure as code, and deployment standards. Integrate Microsoft Fabric with ERP, CRM, telematics, and other systems. Establish guardrails for AI-assisted development, automate workflows, monitor cost and reliability, and create repeatable engineering patterns in partnership with IT.