AI/ML Software Engineer (Hybrid, W2 Role)
Title: AI/ML Software EngineerLocation: Annapolis, MD - Primarily Remote (with occasional onsite requirements) Duration: Long Term ContractJob Description (Exact Responsibilities Extracted)Core Role SummaryThe 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 Responsibilities1. System Design & CollaborationWork within constraints of infrastructure, programming languages, and model selectionContribute to technical decisions (data processing, retrieval, system integration)Collaborate on agent architectures, workflows, and system designDecide when to use LLM vs non-LLM approachesDesign and build AI/ML-driven systems for automation and user support2. Testing, Evaluation & Quality AssuranceDesign and implement testing/evaluation pipelines for AI/ML systemsDevelop unit and integration tests for AI workflows and data pipelinesUse synthetic data for benchmarking and evaluationImprove system performance (accuracy, latency, cost efficiency)3. Deployment & OperationsDeploy AI/ML applications in hybrid cloud environmentsWork with containerized applications (e.g., Docker)Optimize systems for limited compute environments (low GPU availability)4. General ResponsibilitiesDeliver production-grade systems aligned with requirementsContinuously improve tools through iterative developmentDocument system designs, workflows, and technical decisionsStay updated on AI/ML advancements and apply them appropriately5. 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 systemsRedaction of sensitive data (PII detection)Deep research systems (graph-based AI)Document analysis and generationAI agents for automation and workflows6. Delivery ExpectationsBuild production-ready AI systems with Dockerized deploymentsCreate test pipelines and evaluation frameworksDevelop APIs, data pipelines, and backend servicesIntegrate AI solutions into real-world workflows and reporting systemsEnsure compliance, privacy, and performance standards