Forward Deployed AI Engineer, Senior

LCG, Inc.•Rockville, MD

4d•$135,000 - $152,000•Hybrid

About The Position

LCG is seeking a Forward Deployed AI Engineer, Senior to support federal client environments by designing, prototyping, evaluating, and delivering applied AI solutions that solve real operational and mission challenges. This role is ideal for a hands-on AI professional who can work directly with clients, understand business needs, identify where AI can create measurable value, and rapidly translate those needs into working solutions. The Forward Deployed AI Engineer will serve as the AI expert embedded across multiple client projects, partnering with engineering teams, business stakeholders, and leadership to advance AI adoption, improve workflows, and modernize existing systems. The successful candidate will bring strong experience in AI/ML, Python, LLMs, RAG, model evaluation, and AI-assisted development tools. This person should be comfortable operating in ambiguous environments, communicating with technical and non-technical stakeholders, and mentoring teams on practical AI use cases and responsible AI practices.

Requirements

Bachelor’s degree in Computer Science, Engineering, Data Science, Artificial Intelligence, or a related field.
5+ years of hands-on experience in AI/ML engineering, data science, software engineering, or related technical roles.
Strong Python development experience.
Hands-on experience with Generative AI, LLMs, RAG, prompt engineering, and AI-enabled workflow automation.
Experience with at least one agentic or LLM application framework such as LangChain, AutoGen, CrewAI, LlamaIndex, LangGraph, or similar.
Strong understanding of RAG architecture, including retrieval design, embeddings, indexing, chunking strategies, and search quality.
Applied experience evaluating models and AI systems using measurable metrics and documented baselines.
Experience working with cloud AI platforms such as AWS AI/ML services, Amazon Bedrock, AWS SageMaker, Azure AI, Azure OpenAI, or similar platforms.
Experience integrating AI capabilities with APIs, enterprise systems, cloud services, or operational workflows.
Familiarity with Git, CI/CD, secure development practices, and modern SDLC processes.
Hands-on experience using AI-assisted development tools such as Codex, Claude Code, Cursor, or GitHub Copilot to accelerate coding, debugging, documentation, and modernization activities.
Ability to evaluate and validate AI-generated code, identify errors or gaps, and apply strong engineering judgment before implementation.
Ability to communicate effectively with technical teams, business stakeholders, and client leadership.
Ability to operate independently with minimal direction in fast-moving, ambiguous environments.
Ability to obtain and maintain a Public Trust clearance.

Nice To Haves

Master’s degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, or a related field.
Experience with data science, statistical modeling, data quality, or model performance evaluation.
Experience supporting AI adoption, mentoring developers, or leading technical enablement sessions.
Experience with MCP tool-use patterns or enterprise AI integrations.
Experience with GraphRAG, advanced RAG, hybrid search, re-ranking, LoRA/QLoRA, fine-tuning, distillation, pruning, or quantization.
Experience delivering AI solutions in federal, regulated, healthcare, financial, or high-compliance environments.
Experience with FedRAMP-authorized cloud environments or secure federal cloud delivery.
Experience producing model cards, evaluation reports, governance documentation, or responsible AI artifacts.
Experience working in startup, consulting, product, FinTech, or other fast-paced delivery environments.

Responsibilities

Lead discovery sessions with federal clients to understand mission needs, business processes, existing systems, data constraints, and operational pain points.
Translate ambiguous client problems into well-scoped, measurable AI use cases.
Identify opportunities where AI can improve productivity, reduce technical debt, accelerate modernization, or enhance operational workflows.
Communicate AI capabilities, limitations, risks, and expected outcomes clearly to both technical and non-technical stakeholders.
Partner with internal leadership to capture client needs, identify growth opportunities, and align AI solutions with broader modernization strategy.
Own end-to-end AI proof-of-concept delivery, from requirements intake through demonstration and evaluation.
Build working prototypes using Python, LLMs, RAG, agentic frameworks, and cloud AI services.
Develop solutions using tools and frameworks such as LangChain, AutoGen, CrewAI, or similar technologies.
Design and implement RAG or GraphRAG pipelines, including chunking, embedding, indexing, hybrid search, re-ranking, and retrieval evaluation.
Quickly validate whether an AI use case should advance toward production or be retired based on evidence and measurable value.
Document findings, lessons learned, and recommended next steps for client and internal leadership review.
Integrate AI capabilities into existing client systems and operational workflows.
Collaborate with software engineering, DevSecOps, data, and security teams to make AI solutions practical, secure, and maintainable.
Support AI-assisted modernization efforts, including code understanding, code refactoring, test generation, documentation generation, and technical debt reduction.
Apply model optimization techniques such as prompt optimization, caching, latency tuning, throughput tuning, and cost-per-token reduction.
Use Git, CI/CD practices, and secure cloud development standards to support repeatable delivery.
Design and execute evaluation frameworks for AI, ML, LLM, NLP, and RAG-based systems.
Measure model and system performance using metrics such as accuracy, precision, recall, F1 score, hallucination rate, groundedness, cosine similarity, ROUGE, BLEU, AUC-ROC, latency, drift, and cost per token.
Establish baselines, document results, and clearly explain tradeoffs to stakeholders.
Apply responsible AI practices, including transparency, auditability, human review, bias awareness, data protection, and validation.
Produce evaluation documentation, governance artifacts, and recommendations suitable for client review.
Serve as an AI subject matter expert embedded with client and internal engineering teams.
Mentor developers and project teams on practical AI usage, AI-assisted coding, prompt engineering, model evaluation, and responsible AI practices.
Demonstrate tools such as GitHub Copilot, Cursor, Claude Code, Codex, or similar AI-assisted development platforms.
Lead knowledge-sharing sessions, demos, brown bags, and informal training to help teams understand and adopt AI effectively.
Support AI literacy across projects by explaining what AI can and cannot do in practical business terms.