Forward Deployed AI Engineer, Senior

Job Description

Job Description

Location: Rockville, MD (A minimum of 1 day - potentially more depending on clients needs)

Required Clearance: Ability to obtain Public Trust

Sponsorship: No sponsorship assistance is available for this position now or in the future.

Position Description: 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.

Key Responsibilities

Client Discovery and Business Translation

• 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.

AI Prototyping and Proof of Concept Delivery

• 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.

AI Engineering and Workflow Integration

• 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.

Model Evaluation and Responsible AI

• 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.

AI Enablement, Mentorship, and Adoption

• 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.

Required Qualifications

• 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.

Preferred Qualifications

• 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.

Key Skills and Competencies

• Strong applied AI engineering depth with the ability to design, build, evaluate, and improve AI systems.
• Strong business translation skills with the ability to turn client needs into practical AI use cases.
• Strong communication skills and ability to influence teams without formal authority.
• Strong mentoring mindset and ability to teach developers how to apply AI tools effectively.
• Strong evaluation mindset, including the ability to question AI-generated outputs and validate quality.
• Comfortable balancing experimentation with production readiness.
• Ability to work directly with clients and internal teams to identify value, define scope, and deliver measurable outcomes.
• Innovative, proactive, and comfortable working in undefined or evolving environments.

Tools and Technologies

• Python
• LangChain, AutoGen, CrewAI, LlamaIndex, LangGraph, or similar
• RAG, GraphRAG, embeddings, vector databases, hybrid search, re-ranking
• AWS AI/ML, AWS SageMaker, Amazon Bedrock
• Azure AI, Azure OpenAI
• Git, GitHub, CI/CD
• GitHub Copilot, Cursor, Claude Code, Codex, or similar AI coding tools
• APIs, MCP tool-use patterns, cloud services, and enterprise workflow integrations

• Model evaluation frameworks and AI governance documentation

Compensation and Benefits

The projected compensation range for this position is $135,000 to $152,000 per year benchmarked in the Washington, D.C. metropolitan area. The salary range provided is a good faith estimate representative of all experience levels. Salary at LCG is determined by various factors, including but not limited to role, location, the combination of education/training, knowledge, skills, competencies, certifications, and work experience.

LCG offers a competitive, comprehensive benefits package which includes health insurance options (medical, dental, vision), life and disability insurance, retirement plan contributions, as well as paid leave, federal holidays, professional development, and lifestyle benefits.

Devoted to Fair and Inclusive Practices

All qualified applicants will receive consideration for employment without regard to sex, race, ethnicity, age, national origin, citizenship, religion, physical or mental disability, medical condition, genetic information, pregnancy, family structure, marital status, ancestry, domestic partner status, sexual orientation, gender identity or expression, veteran or military status, or any other basis prohibited by law.

If you are interested in applying for employment with LCG and need special assistance or an accommodation to apply for a posted position, contact our Human Resources department by email at View email address on ziprecruiter.com.

Securing Your Data

Beware of fraudulent job offers using LCG's name. LCG will never request payment-related details or advancement of money during the application process. Legitimate communication will only come from lcginc.com or View email address on ziprecruiter.com emails, not free commercial services like Gmail or WhatsApp. If you receive suspicious emails asking for payment or personal information, contact us immediately at View email address on ziprecruiter.com.

If you believe you are the victim of a scam, contact your local law enforcement and report the incident to the U.S. Federal Trade Commission.

Use of Artificial Intelligence in Recruiting

LCG may use artificial intelligence (AI) and other automated technologies to support portions of the recruiting and hiring process, including resume review, candidate matching, interview scheduling, skills assessment, and other administrative functions. AI-assisted tools are used solely to support our hiring process and do not independently determine employment outcomes. Final hiring decisions are made by trained hiring professionals following a comprehensive review of each candidate's qualifications. We are committed to equal employment opportunity and strive to ensure our hiring practices are fair, transparent, and compliant with applicable laws. Applicants needing a reasonable accommodation during the application or interview process should contact Human Resources.