AI/ML Software Engineer (W2 Role)

Title: AI/ML Software Engineer

Location: Annapolis, MD - Primarily Remote (with occasional onsite requirements)

Duration: Long Term Contract

Job Description (Exact Responsibilities Extracted)

Core Role Summary

The AI/ML Software Engineer will design and build AI-powered software systems to automate tasks, support internal users, and enhance user experience across client systems.

Exact Job Responsibilities

• System Design & Collaboration
• Work within constraints of infrastructure, programming languages, and model selection
• Contribute to technical decisions (data processing, retrieval, system integration)
• Collaborate on agent architectures, workflows, and system design
• Decide when to use LLM vs non-LLM approaches
• Design and build AI/ML-driven systems for automation and user support
• Testing, Evaluation & Quality Assurance
• Design and implement testing/evaluation pipelines for AI/ML systems
• Develop unit and integration tests for AI workflows and data pipelines
• Use synthetic data for benchmarking and evaluation
• Improve system performance (accuracy, latency, cost efficiency)
• Deployment & Operations
• Deploy AI/ML applications in hybrid cloud environments
• Work with containerized applications (e.g., Docker)
• Optimize systems for limited compute environments (low GPU availability)
• General Responsibilities
• Deliver production-grade systems aligned with requirements
• Continuously improve tools through iterative development
• Document system designs, workflows, and technical decisions
• Stay updated on AI/ML advancements and apply them appropriately
• Key Functional Work Areas (Across Project Lifecycle)

The role involves hands-on development across multiple AI domains:

• Chatbot development (internal & external)
• Robotic Process Automation (RPA)
• Knowledge retrieval (RAG, search systems)
• Translation and transcription systems
• Redaction of sensitive data (PII detection)
• Deep research systems (graph-based AI)
• Document analysis and generation
• AI agents for automation and workflows
• Delivery Expectations
• Build production-ready AI systems with Dockerized deployments
• Create test pipelines and evaluation frameworks
• Develop APIs, data pipelines, and backend services
• Integrate AI solutions into real-world workflows and reporting systems
• Ensure compliance, privacy, and performance standards