Right Data. Best Decisions. | Technology and deep data expertise to drive the best defense and intelligence decisions.
Distinguished AI/ML Engineer
Location
Washington
Posted
5 days ago
Salary
$190K - $220K / year
Seniority
Lead
Job Description
Distinguished AI/ML Engineer
FTI - Frontier Technology Inc.
• Architect and integrate hybrid AI systems that combine traditional machine learning, deep learning, large language models (LLMs), and retrieval-augmented generation (RAG) pipelines. • Design and deploy scalable AI architectures including APIs, microservices, and model-serving frameworks that integrate seamlessly with analytic, simulation, or operational systems. • Lead the full AI/ML lifecycle — from data ingestion and feature engineering through training, deployment, and sustainment within secure DoD environments (IL5/IL6, ATO, GovCloud). • Engineer event-driven data pipelines and feature stores for both structured and unstructured data, including text, imagery, and simulation outputs. • Ensure Responsible AI practices by embedding traceability, explainability, and confidence scoring into deployed systems. • Implement and maintain MLOps pipelines (MLflow, Kubeflow, Airflow, Docker/Kubernetes) to support continuous integration, retraining, and drift detection. • Transition R&D prototypes into production, optimizing for mission constraints such as limited compute, edge environments, or disconnected operations. • Provide technical leadership and mentorship, setting standards for model quality, architectural design, and ethical AI deployment across programs. • Collaborate across engineering, data, and modeling teams to unify FTI’s AI portfolio, ensuring interoperability and reuse across mission systems. • Support proposal and solution development, providing technical inputs for AI/ML architectures, data strategies, and Responsible AI assurance frameworks.
Job Requirements
- Active Secret clearance required; TS/SCI strongly preferred.
- Bachelor’s degree in Computer Science, Engineering, or a related technical field (Master’s or Ph.D. preferred).
- 10+ years of overall experience in AI/ML development, with 5+ years designing and deploying scalable AI/ML architectures, including at least two full lifecycle implementations (from prototype to operational system).
- Proficiency in Python, PyTorch, TensorFlow, and modern ML frameworks.
- Experience designing or deploying systems using vector databases (Milvus, Pinecone, Weaviate), knowledge graphs, and semantic search frameworks.
- Proven ability to design event-driven data pipelines using Databricks, Spark, Flink, or Kafka.
- Demonstrated experience deploying AI/ML systems in secure, classified, or edge environments.
- Familiarity with Responsible AI and assurance principles, including bias detection, explainability, human-machine teaming, and hallucination prevention.
- Experience integrating AI models into simulation, modeling, or operational planning systems is highly desirable.
- Strong communication and mentoring skills, with the ability to lead technically while remaining deeply hands-on.
Benefits
- FTI Defense offers a variety of benefits including health insurance and opportunities for professional development.
Related Guides
Related Job Pages
More AI Engineer Jobs
• Responsible for researching, designing, developing, and implementing AI solutions that enhance Identity, Credential, and Access Management (ICAM) capabilities • Collaborate with identity architects, cybersecurity engineers, data analysts, software developers, and mission stakeholders • Identify opportunities where AI can improve identity analytics, access governance, operational efficiency, cybersecurity, and decision support • Support the full lifecycle of AI initiatives from concept development and experimentation through production implementation and sustainment • Analyze large volumes of authentication, authorization, audit, entitlement, and access governance data to identify trends, risks, and actionable insights • Collaborate with engineers and architects to integrate AI capabilities into existing ICAM platforms and enterprise services
• Define and drive the technical strategy for the agentic platform • Frame and prioritize the highest-impact AI problems • Establish the evaluation and quality framework for AI systems • Set the architecture for trust • Lead the design and delivery of large, cross-team initiatives • Drive adoption of shared agentic capabilities • Act as technical authority for critical decisions and trade-offs • Mentor senior engineers and partner with Product/UX/Applied AI on long-term roadmap
• Implement and maintain clean, well-tested code for MCP tools, agentic features, and their supporting services (front-end and back-end), to team standards • Integrate LLMs and models via APIs/SDKs to deliver agentic workflows, with attention to correctness, latency, and cost • Help build the guardrails of agentic workflows — human-in-the-loop steps, traceability, and clear error handling • Contribute to automated tests, evaluation harnesses for AI behavior, CI/CD pipelines, and developer tooling • Debug and troubleshoot across the stack (agent/tool logic, APIs, web UI), collaborating with senior engineers and QA • Work with Product and UX to translate requirements into technical tasks; surface risks and blockers early and seek mentorship
Senior AI Platform Engineer
VIAFLOW®Aceleramos a transformação de negócios com design, produtos digitais e soluções de hiperautomação.
• Architect the project's technical foundation, ensuring scalable, secure and observable environments. • Combine Kubernetes, automation, proactive security and FinOps to support our services. • Design, implement and evolve cloud and Kubernetes environments for running applications, APIs, agents, AI services and data pipelines. • Build and maintain CI/CD pipelines, focusing on security, standardization, traceability and delivery speed. • Automate infrastructure provisioning using Infrastructure as Code practices. • Implement observability standards, including logs, metrics, tracing, dashboards, alerts and platform health indicators. • Support the operation of AI workloads, including inference services, embeddings, agent orchestration and integration components. • Ensure best practices for security, environment segregation, secrets management, access control and infrastructure policies. • Work on performance, availability, scalability, resilience and cost optimization. • Create reusable patterns for environments, deployments, monitoring, security and operations. • Work closely with AI, Data, Product, Full Stack and Machine Learning teams to translate technical needs into platform capabilities.



