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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in developing production-ready AI applications, particularly with Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems. Proficient in prompt engineering, embeddings, and evaluating AI model performance to drive continuous improvement and enhance AI capabilities.
Highest-signal resume keywords
Production AI Application DevelopmentRetrieval-Augmented Generation (RAG) SystemsPrompt EngineeringPython ProgrammingAI Frameworks (LangGraph, LangChain, etc.)
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Large Language Models (LLMs)Retrieval-Augmented Generation (RAG)Prompt EngineeringEmbeddingsSemantic SearchVector DatabasesAI Model Performance EvaluationExperimentation FrameworksPython ProgrammingCloud-Native Applications
Soft Skills
Analytical SkillsProblem-Solving SkillsCommunication SkillsIntellect and CuriosityCollaboration
Tools & Technologies
LangGraphLangChainLlamaIndexSemantic KernelCrewAIAutoGenREST APIsDistributed Software Systems
Industry Keywords
Artificial IntelligenceMachine LearningSoftware EngineeringData ScienceAI Systems Development
Tech Stack
Tools & technologiesCloudPython
About the role
Key responsibilities & impact- Design, prototype, and implement production-ready AI capabilities that improve reasoning, accuracy, explainability, and developer productivity.
- Evaluate emerging LLMs, multimodal models, agent frameworks, and AI techniques to identify opportunities for platform advancement.
- Rapidly prototype new AI capabilities and transition successful experiments into production.
- Improve autonomous and multi-agent workflows through prompt engineering, reasoning optimization, memory strategies, tool selection, and context management.
- Continuously refine Retrieval-Augmented Generation (RAG) pipelines, retrieval strategies, embeddings, reranking, and grounding techniques.
- Improve AI response quality through experimentation, benchmarking, and iterative optimization.
- Develop evaluation frameworks that measure accuracy, groundedness, explainability, latency, and overall AI effectiveness.
- Build benchmark datasets, automated evaluation pipelines, and performance metrics for production AI systems.
- Analyze AI failures, hallucinations, retrieval gaps, and reasoning errors to drive continuous improvement.
- Collaborate with software engineers to improve knowledge ingestion, document processing, semantic search, embeddings, and enterprise knowledge management.
- Design approaches that maximize retrieval quality across large technical documentation and source code repositories.
- Improve how AI agents discover, organize, and reason over enterprise knowledge.
- Partner closely with AI architects, platform engineers, software engineers, and front-end engineers to improve the overall intelligence of the platform.
- Share research findings, experimental results, and engineering recommendations with cross-functional teams.
- Help establish best practices for experimentation, evaluation, and AI quality throughout the organization.
Requirements
What you’ll need- Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, Software Engineering, Engineering, Data Science, or a related technical discipline (or equivalent professional experience).
- 5+ years of software engineering, applied AI, machine learning, or AI systems development experience.
- Demonstrated experience developing production AI applications powered by Large Language Models (LLMs).
- Experience designing and optimizing Retrieval-Augmented Generation (RAG) systems.
- Experience with prompt engineering, embeddings, semantic search, vector databases, and knowledge retrieval.
- Experience evaluating AI model performance and implementing experimentation frameworks.
- Strong programming skills in Python and experience with modern software engineering practices.
- Experience with AI frameworks such as LangGraph, LangChain, LlamaIndex, Semantic Kernel, CrewAI, AutoGen, or similar.
- Experience with using AI coding assistants as part of your daily workflow.
- Familiarity with REST APIs, cloud-native applications, and distributed software systems.
- Strong analytical, problem-solving, and communication skills.
- Intellect and curiosity for AI systems and how they behave.
- Deep passion for experimenting with new AI techniques.
- Background in evaluation, explainability, and continuous improvement.
- Proven success with ownership of difficult technical challenges and collaboration across disciplines.
Benefits
Comp & perks- The Opportunity to support high-visibility federal missions
- A culture that values innovation, growth, and collaboration
- Access to cutting-edge tools and technologies
- Comprehensive benefits for you and your family
- A career path that rewards ambition and performance