AI Agent Software Engineer (MCP, LLMs, RAG, etc.)

This role builds production-grade AI systems that ingest messy real-world data, reason over it, and deliver operational value. The AI Software Engineer designs, implements, and deploys models and pipelines that support analytics, automation, and decision advantage across government and consortium use cases.

This is not a research sandbox role. It’s applied engineering. You will write code that runs in constrained environments, integrates with existing systems, and survives contact with users, security teams, and auditors.

Core Responsibilities
- AI / ML Engineering
- Design, build, and deploy machine learning and AI solutions across structured and unstructured data
- Implement model pipelines for ingestion, training, inference, and monitoring
- Apply techniques such as NLP, entity resolution, classification, anomaly detection, and retrieval-augmented generation
- Balance model performance, explainability, and operational reliability

Software Engineering
- Write clean, testable, production-quality code
- Build APIs and services that expose AI capabilities to downstream systems
- Integrate AI components into larger platforms and workflows
- Participate in code reviews and enforce engineering standards

Data Pipelines & Infrastructure
- Build and maintain data ingestion and processing pipelines
- Work with streaming and batch data sources
- Optimize performance for scale, cost, and latency
- Deploy solutions in cloud, hybrid, or on-prem environments

Operationalization & Security
- Support model deployment in regulated and security-constrained environments
- Implement monitoring for model drift, performance degradation, and data quality issues
- Collaborate with security, platform, and DevSecOps teams
- Document assumptions, limitations, and operational behaviors

Collaboration
- Work closely with analysts, product leads, and domain experts
- Translate mission needs into technical implementations
- Support demonstrations, pilots, and iterative delivery
- Mentor junior engineers where appropriate

Required Qualifications
- 10+ years of software engineering and understanding of modern languages (e.g, Rust)
- 3+ years of experience building AI or ML systems in production
- Strong proficiency in Python and modern ML frameworks
- Solid software engineering fundamentals (APIs, testing, version control)
- Experience working with real-world, imperfect data
- Ability to operate independently and make sound technical tradeoffs

Preferred Qualifications
- Bachelor’s or Master’s degree in Computer Science, Engineering, or equivalent experience
- Experience with NLP, OSINT data, or large-scale data fusion
- Familiarity with GPU-accelerated or high-performance computing environments
- Experience deploying models in secure or regulated environments
- Knowledge of modern data architectures and message-based systems
- Exposure to government, defense, or intelligence use cases

What Success Looks Like (First 6–12 Months)
- AI capabilities move from prototype to production
- Models are trusted because their behavior is understood
- Data pipelines stop breaking quietly
- Analysts and operators rely on AI outputs rather than work around them
- Technical debt is managed instead of ignored

Growth Path
- Depending on strengths, this role can grow into:
- Senior or Principal AI Engineer
- AI Technical Lead or Architect
- Platform or Product-focused Engineering Lead

This role is for builders who respect reality: data is messy, users are impatient, and systems must work anyway.