Full performance AI/ML Engineer

Markon•McLean, VA

2d•$130,000 - $160,000•Onsite

About The Position

Markon is seeking an AI/ML Engineer (Full Performance) to support an Intelligence Community client in McLean, Virginia. This position will design, develop, integrate, and maintain AI/ML-enabled applications supporting mission-critical enterprise capabilities, with a focus on large language model (LLM) integration, agent-based workflows, and retrieval-augmented generation (RAG). The AI/ML Engineer will work across application development, data engineering, and system integration to deliver scalable, reliable, and production-ready AI solutions that connect with enterprise tools, data sources, and existing systems.

Requirements

Active TS/SCI clearance with Full Scope Polygraph.
Bachelor's degree and at least 3 years of related experience; OR Master's degree and at least 1 year of related experience; OR PhD with no additional experience required; OR 6 years of equivalent relevant work experience and no degree.
Experience developing AI/ML-enabled applications, including LLM integration, agent-based workflows, RAG, or similar retrieval-based capabilities.
Proficiency in Python, common AI/data libraries, and AI frameworks supporting LLM application development.
Experience implementing agent orchestration, tool integration, memory/context management, and connections to external systems.
Strong understanding of AI/ML fundamentals, including transformers, machine learning algorithms, LLM behavior, data processing, and model integration.
Experience developing or integrating REST APIs and Python-based backend services.
Experience with PostgreSQL or other relational databases, including schema design, indexing, and query optimization.
Understanding of production-ready software development practices, including application testing, reliability, deployment, performance optimization, and maintainability.

Nice To Haves

Experience with Model Context Protocol (MCP) or similar protocols connecting AI agents to external tools, data sources, and enterprise systems.
Experience developing full-stack AI or agent-based applications, including frontend interfaces, backend services, orchestration layers, and enterprise deployment.
Experience with FastAPI, Flask, Docker, containerized development environments, and AWS or other cloud platforms.
Experience optimizing LLM and agentic AI workflows, including prompt engineering, tool execution, memory management, context windows, latency reduction, observability, evaluation, and cost/performance tuning.
Experience with MLflow or similar model evaluation and lifecycle tools.
Experience implementing vector databases, embedding models, semantic search, and retrieval configuration.
Experience processing and transforming structured and unstructured data, including PostgreSQL schema refinement and optimization.

Responsibilities

Design, develop, implement, and maintain AI/ML-enabled applications, emphasizing LLM integration, agent-based workflows, and RAG capabilities.
Develop and support agentic AI architectures, including orchestration logic, tool integration, memory management, context handling, and connections to external systems.
Design, implement, and optimize RAG pipelines, including document processing, embedding generation, semantic retrieval, prompt assembly, and response optimization.
Develop and maintain Python-based services, REST APIs, backend components, and user-facing application features supporting enterprise AI solutions.
Integrate AI/ML applications with enterprise tools, databases, and structured or unstructured data sources.
Process, organize, and transform data to support AI model integration, retrieval workflows, and application functionality.
Develop and optimize PostgreSQL database schemas, queries, indexing strategies, and data workflows.
Evaluate and benchmark AI/ML application and model performance using appropriate testing and evaluation methodologies.
Optimize AI applications for reliability, latency, performance, usability, maintainability, and scalability.
Apply software engineering best practices throughout the application development lifecycle, including testing, deployment, troubleshooting, and ongoing sustainment.
Collaborate with technical stakeholders and development teams to translate mission requirements into functional, production-ready AI/ML capabilities.