SME AI/ML Engineer

Responsibilities

• check_circle Design, develop, and optimize AI and ML solutions to enhance operational and analytical capabilities within a larger enterprise level Data and AI analytics platform.
• check_circle Build and maintain data pipelines for efficient data processing and model training.
• check_circle Train and tune algorithms to improve predictive accuracy and decision-support tasks.
• check_circle Deploy models into production environments, ensuring reliability and performance.
• check_circle Identify and integrate appropriate COTS, government, and custom tools within established frameworks.
• check_circle Collaborate with cross-functional teams to ensure alignment with enterprise architecture and security requirements.
• check_circle Manage project timelines and deliverables, ensuring adherence to quality standards.
• check_circle Facilitate communication between technical teams and stakeholders to align project goals.
• check_circle Stay updated on industry trends and advancements in AI/ML technologies.
• check_circle Develop and implement best practices for model development and deployment.
• check_circle Ensure compliance with cybersecurity policies and standards throughout the project lifecycle.
• check_circle Participate in the Engineering Control Board process for major engineering milestones.
• check_circle Analyze system performance metrics and recommend improvements for efficiency and scalability.

Basic qualifications

• Active Top Secret (TS) clearance with SCI eligibility.
• Bachelor’s degree in Computer Science, Data Science, Artificial Intelligence, Engineering, or related technical discipline and 12+ years of relevant experience OR Master’s degree in a related field and 10+ years of relevant experience.
• Minimum of 10 years of experience in AI/ML and/or data intelligence engineering or related fields.
• Experience developing and deploying AI/ML models using frameworks such as PyTorch, TensorFlow, or equivalent.
• Experience building and integrating data pipelines to support ML workflows.
• Experience deploying containerized AI workloads (e.g., Docker, Kubernetes).
• Experience integrating AI/ML models into CI/CD and DevSecOps environments.
• Experience implementing model evaluation, performance tuning, and lifecycle management practices.
• Proven experience in designing and deploying AI/ML models in production environments.
• Strong programming skills in languages such as Python, R, or Java.
• Experience with data pipeline construction and management.
• Excellent leadership and team management skills.
• Strong problem-solving abilities and analytical thinking.
• Strong communication and interpersonal skills.
• Experience developing Agentic AI solutions, including autonomous planning–execution–reflection loops, multi-agent collaboration and coordination, and tool usage patterns including API integration, retrieval-augmented generation (RAG), and memory/context management
• Solid understanding and hands-on experience with generative AI models including prompt engineering, chain-of-thought reasoning, and Natural Language Processing (NLP) tasks such as entity extraction, summarization, and semantic search.
• Working knowledge of Large Language Models (LLMs) and agent frameworks such as LangChain, LangGraph, CrewAI, A2A, MCP, or AutoGen
• Experience using vector databases (e.g., Pinecone, Weaviate, FAISS)
• Familiarity with deployment into virtualized and containerized environments (e.g., VMware, Docker, Kubernetes)

Preferred qualifications

• Active TS/SCI clearance.
• 12+ years of experience in AI/ML and/or data intelligence engineering or related fields.
• SAFe Agilist (SA) or related SAFe certification.
• Experience operating within SAFe or large-scale Agile frameworks supporting enterprise systems.
• Experience supporting AI/ML solutions across multi-enclave DoD environments.
• Experience implementing automated model validation, bias detection, and drift monitoring frameworks.
• Experience integrating AI services into enterprise APIs and microservices architectures.
• Experience optimizing GPU-based training and inference pipelines.
• Experience supporting enterprise-scale analytics, data platforms, or AI modernization initiatives.
• Experience with cloud-based AI/ML platforms and tools.
• Familiarity with cybersecurity principles and practices.
• Experience in a government or defense contracting environment.
• Experience designing and implementing safety, guardrails, and bias-mitigation strategies for autonomous agents and multiagent systems
• Experience integrating agents with cloud-native workflows, streaming data pipelines, and real-time decision-making environments
• Familiarity with evaluation and observability tools for AI agents, such as LangSmith, OpenAI Evals, or custom telemetry systems
• Experience with AI service integration such as NIMS, Azure OpenAI, Bedrock, GCP Vertex AI
• Hands-on GPU programming experience for ML workloads using CUDA, PyTorch, or TensorFlow, including optimization for performance and efficiency.

About the company