Top AI Engineer Jobs

As an AI Engineer, you will develop, train, and deploy machine learning models, collaborate with stakeholders, and contribute to ML strategies and solutions for Global Defense.

As a Principal Machine Learning Engineer, you'll design and implement core ML systems for AI agents, collaborating with cross-functional teams and overseeing the full lifecycle of products from data strategy to deployment.

The AI Engineer will design, build, and deploy advanced AI systems, focusing on Large Language Models and agentic AI solutions within the Vantage platform, while collaborating with teams to meet user needs in an Agile environment.

Develop and integrate locally hosted LLM and ML capabilities into accredited, air-gapped government applications. Build data pipelines, REST API integrations, logging/audit instrumentation, and support deployment and monitoring in classified environments. Produce testable, documented code and collaborate with program stakeholders while following security and AI governance requirements.

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.

Lead QA strategy for large-scale Generative AI products and platforms. Design scalable testing and automation across AI pipelines, APIs, UIs, microservices, and SaaS. Establish release-readiness gates, diagnose model degradation and system failures, drive root-cause analysis, and mentor QA engineers while collaborating with Product, Engineering, DevOps, UX, and Customer Engineering.

Own and ship end-to-end production features using agentic development. Build and operate multi-agent workflows, validate agent output, work in brownfield and greenfield environments, pair with engineers to spread agentic practices, and maintain long-term quality and reliability of systems.

Design, prototype, and productionize AI-enabled software in cloud environments (Azure). Collaborate with clinical and business users, iterate on prototypes, maintain code quality, contribute to data pipelines and infrastructure, and follow data governance and PHI security requirements.

Build and operate cloud infrastructure for an AI platform: design Terraform modules, run production Kubernetes, manage CI/CD and release engineering, implement observability and SLOs, enforce security/compliance controls, manage secrets and certificates, and support incident response and platform readiness.

Design, build, and operate scalable data pipelines and ETL for AI/ML. Create model-ready datasets (features, embeddings, prompts), implement storage layers (data lakehouse, vector stores), optimize low-latency retrieval for inference and RAG, tune search/indexing with ElasticSearch/OpenSearch, collaborate with analytics and product teams, and maintain CI/CD, version control, and Linux-based performance tuning.

Design and build agentic AI systems and internal coding/research agents; architect and analyze inference workload dynamics; collaborate with evaluation teams to improve software and hardware evaluation quality.

Design, build, and operate scalable, cloud-native AI inference platforms for production ML workloads. Optimize GPU utilization and inference performance, implement autoscaling, monitoring, security, and collaborate with ML teams to deploy and support enterprise AI models.

Lead technical strategy and architecture for an AI Data Engine to accelerate ML dataset generation and data warehouse operations. Hands-on staff engineer mentoring engineers, designing scalable storage, vector retrieval, LLM/agentic workflows, and collaborating with ML research and platform teams to deliver petabyte-scale data solutions.

Build AI-powered platforms and tools, focusing on backend services in Python, integrating LLM capabilities, and ensuring performance and reliability.

The role involves designing, training, and evaluating ML models, optimizing performance, and collaborating with teams to implement AI technologies in products.

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.

As a Senior AI Solutions Engineer, you will build and maintain AI agents, design workflows, and ensure compliance with data standards, collaborating across teams.

Lead integration of AI/LLMs into enterprise SaaS and desktop products, evangelize AI-first practices, design and implement scalable .NET/C# backends, modernize legacy architectures, mentor engineers, and collaborate with product and QA across the full development lifecycle to deliver AI-enabled features and agentic workflows.

Senior technical leader who architects, builds, and deploys Generative and Agentic AI solutions for enterprise clients. Leads client engagements, mentors technical teams, designs scalable ML/LLM pipelines and MLOps, facilitates workshops, and ensures best practices and responsible AI across projects.

Lead design and development of AI-driven document compliance services using VLMs and LLMs. Build prompt workflows, evaluation pipelines, Python/FastAPI AI services, optimize inference cost and performance, collaborate with product and domain SMEs, and drive architecture, mentoring, and production reliability for scalable AI platform capabilities.

Design, build, and operate cloud-scale, multi-tenant services for Microsoft Edge and Copilot app management. Drive architecture, reliability, security, and cross-team integration from design through production. Mentor engineers, lead technical initiatives, and use telemetry to improve product quality and customer experience.

Senior DevOps/Cloud Engineer responsible for designing, automating, deploying, and operating cloud infrastructure across AWS and GCP. Build CI/CD pipelines, automate configuration with Ansible and Python, support AI/ML platform deployments, manage containers with Docker/Kubernetes, work within compliance frameworks (FedRAMP/NIST), and troubleshoot production issues.

Lead technical pre-sales engagements to architect, prototype, and deploy scalable, secure cloud and AI solutions on Azure. Drive migrations, containerization (AKS), hybrid networking, CI/CD, and modernized architectures while advising founders and engineering leaders and enabling production deployments.

Design, develop, and sustain cloud-enabled AI software and scalable search/retrieval systems. Build LLM-powered features and RAG pipelines, implement hybrid BM25+vector search, develop React frontends and Node.js backends, perform testing, code reviews, and collaborate across Agile teams and security/operations.

Design, build, and operate scalable GenAI platforms (AI Agents, RAG, Knowledge Graphs). Develop backend services, implement IaC and CI/CD, deploy on cloud-native and Kubernetes environments, and collaborate with data scientists and ML engineers to deliver production-grade AI infrastructure.