Top AI Engineer Jobs

Own end-to-end customer data migrations from legacy databases and files into GovPilot. Use AI coding agents to profile data, create mappings, generate transformation and loading scripts, automate validation, and build reusable migration pipelines. Review and correct AI-generated SQL and scripts, identify data quality issues, maintain auditable migration logic, manage multiple client migrations, and support evening or weekend production go-lives.

Leads enterprise adoption of AI, machine learning, generative AI, and intelligent automation across federated engineering teams. Defines reference architectures, standards, reusable patterns, and Responsible AI governance while advising delivery teams on secure, resilient, scalable implementations. Influences senior stakeholders, promotes AI-assisted engineering practices, establishes validation standards, and builds communities of practice to improve consistency, delivery speed, and operational readiness.

Leads development of a next-generation equipment and asset finance platform. Designs, builds, tests, secures, and troubleshoots scalable Java and Spring Boot applications using microservices, REST, Kafka, React, and relational databases. Provides technical direction to a global feature team, mentors engineers, reviews code, drives AI-assisted engineering practices, and contributes to architecture. Improves application stability, observability, code quality, deployment reliability, and operational support across development, staging, and production environments.

Leads and supports systems engineering for complex DoD and intelligence community programs, including requirements engineering, architecture, integration, testing, reliability, cloud solution analysis, risk management, and technical planning. Advises government stakeholders, leads trade studies and engineering assessments, integrates Maven across programs, and oversees systems engineering teams. The role requires extensive expert-level experience with DoD or IC acquisition, requirements processes, systems-of-systems or cloud environments, and GEOINT-related missions.

Develop and maintain production AI/ML applications centered on agent-based workflows, LLM integration, and RAG pipelines. Build Python REST APIs, integrate external systems and AI frameworks, support full-stack application development, process structured and unstructured data, refine Postgres schemas, and optimize applications for reliability, performance, usability, and scalability. The role requires an active TS/SCI clearance with polygraph and full-time onsite work.

Build and maintain data ingestion, transformation, and ETL/ELT pipelines; support data modeling, warehousing, quality validation, governance, cloud platform deployment, and analytics environments. Collaborate with analysts, data scientists, and product teams, participate in Agile ceremonies and code reviews, and document data flows, lineage, and standards.

Design, train, test, deploy, monitor, and maintain ML models (including LLMs/generative AI) for national security missions. Collaborate with data engineers, software developers, data scientists, and stakeholders to apply ML algorithms, triage large datasets, and deliver production-safe, mission-critical AI/ML solutions.

Owns the architecture and evolution of an enterprise AI platform supporting agent orchestration, retrieval systems, production LLM applications, governance, evaluation, safety, and multi-region cloud operations. Leads platform strategy, reliability, observability, incident response, security, cost management, and engineering standards. Mentors engineers and partners across product, engineering, data, security, and delivery teams to deploy scalable, measurable, enterprise-grade AI solutions.

Lead technical pre-sales engagements for Microsoft 365 Copilot, Copilot Studio, AI agents, and cloud endpoint solutions. Deliver demonstrations, discovery sessions, whiteboarding, solution designs, and proof-of-concept support. Develop technical win strategies, address objections and blockers, collaborate with partners and internal teams, educate customers, and guide adoption while embedding governance, security, and compliance into proposed solutions.

Design, prototype, and productionize Generative AI and LLM features across backend and frontend. Integrate ML models, fine-tune, perform prompt engineering, operate inference platforms, and support AI-based product launches while participating in on-call duties.

Leads technical pre-sales engagements for enterprise AI workforce solutions. Develops win strategies, architecture designs, demonstrations, whiteboarding sessions, and proof of concepts. Advises customers on secure, scalable Microsoft solutions, coordinates stakeholders, removes technical blockers, mentors engineers, and provides product feedback. Expertise is required in either Microsoft 365 Copilot or Microsoft Cloud Endpoint technologies.

Build and operate shared AI platform capabilities, including SDKs, APIs, agent infrastructure, durable workflows, model integrations, retrieval, evaluation, and debugging tools. The role focuses on reliability, observability, performance, secure customer-data handling, and turning AI experiments into reusable production capabilities. Responsibilities also include partnering with product teams, driving adoption, diagnosing AI failures, and mentoring or leading broader technical initiatives.

Design, deploy, and operate production-grade agentic AI systems, including multi-agent orchestration, RAG pipelines, LLM integrations, evaluation harnesses, observability, and cost and safety monitoring. Collaborate directly with client development teams, lead technical design sessions, establish reusable AI engineering patterns, and measure system performance across accuracy, latency, safety, and cost.

Leads enterprise technology, architecture, and AI strategy across transformation initiatives. Defines technical roadmaps, architectural standards, governance frameworks, reference architectures, and responsible AI practices. Evaluates emerging technologies, drives proofs of concept and modernization, aligns investments with business priorities, and accelerates AI adoption. Partners with executives and cross-functional leaders, influences technical direction without direct authority, mentors senior engineers and architects, and represents the organization as a technology and AI thought leader.

Build and operate production software features incorporating agentic AI capabilities into APIs, services, and applications. Develop scalable backend systems, distributed services, data integrations, observability, and AI evaluation workflows. Collaborate with product, design, and platform teams, and participate in code reviews, design discussions, and security reviews. The role requires Python or similar backend development, LLM and generative AI experience, agentic AI patterns, REST APIs, databases, queues, and third-party integrations.

Design, build, and sustain AI analytics stack components including Python backends, FastAPI services, LLM-enabled agentic workflows, and Streamlit/React frontends. Implement CI/CD and Docker-based deployments, integrate relational databases (Postgres/MySQL), and ensure secure, maintainable delivery. Collaborate cross-functionally to prototype, deploy, and operate AI-enabled user interfaces and APIs to support mission outcomes in a cleared, on-site environment.

Designs and develops AI-enabled application security solutions, vulnerability assessment pipelines, and DevSecOps integrations. Builds connections among source repositories, security tools, vulnerability platforms, enterprise workflows, and LLM systems. Establishes safeguards for AI-generated code and applications, addresses prompt injection and data exposure risks, leads risk assessments and remediation, and develops security metrics. Collaborates with application security, engineering, AI, and platform teams while providing technical leadership and incident response support.

Develop and maintain production-ready full-stack software using Java, Spring Boot, React.js, AWS, microservices, and DevOps practices. Design scalable, reliable architectures; write and review code; test and operate products; analyze failures; and improve software reliability. Build generative AI applications using LLMs, RAG, vector databases, semantic search, Azure OpenAI, OpenAI APIs, LangChain, LangGraph, MCP, and AI agents while following security, compliance, governance, Agile, and SRE standards.

Design and operate AI-powered multi-agent systems for incident triage, root-cause analysis, and self-service debugging. Build knowledge bases, ingestion and retrieval pipelines, AWS infrastructure, evaluation frameworks, guardrails, and operational documentation. Partner with platform, operations, software, and domain teams to convert production incidents into reliable AI tools and deploy them into audited production environments.

Build and ship full-stack AI visual creation products, including music videos, generative cover art, and animated video covers. Develop preference data pipelines, integrate and evaluate image and video generation models, and create agentic AI director workflows. Collaborate with designers and product managers to shape product scope and improve visual quality.

Leads the technical architecture and strategy for enterprise AI solutions. Designs and deploys production-grade RAG systems, agentic workflows, LLM orchestration, and predictive analytics platforms. Builds scalable, secure, observable, multi-tenant AI systems while managing performance and costs. Partners with product, data science, and engineering teams, mentors developers, establishes technical standards, and influences technology roadmaps.

Own and evolve the AI Accountant’s full-stack systems, with emphasis on backend architecture, reliable third-party data ingestion, idempotent processing, background jobs, account synchronization, and production operations. Integrate LLMs and ML into classification and reconciliation workflows with strong validation and failure handling. Design PostgreSQL schemas and ensure transactional correctness while shipping frequently and owning systems end to end.

Build and operate the AI platform powering enterprise agents and AI applications. Responsibilities include developing agent orchestration, retrieval pipelines, APIs, guardrails, evaluation frameworks, AI-assisted engineering workflows, cloud infrastructure, CI/CD, observability, and reliability processes. The role collaborates across engineering, product, analytics, delivery, and client teams to deploy secure, measurable, production-ready AI systems.

Leads AI and machine learning data engineering development projects, including global initiatives. Manages resources and development teams, improves quality and engineering practices, provides subject matter expertise, develops business plans, mitigates risks, handles escalations, and drives technology innovation and change management.

Develop and maintain web applications incorporating generative AI for a government customer. Build C#/.NET and JavaScript solutions, APIs, backend services, SQL integrations, and cloud deployments using AWS and Microsoft services. Integrate LLMs, AI platforms, MCP servers, and external systems; test, troubleshoot, document, and secure applications. Collaborate with analysts, UX developers, subject-matter experts, and technical teams to deliver practical AI-powered business solutions.