Job Summary
Experience: 4–7+ years in AI/ML engineering, with at least 2+ years in Generative AI / LLM-based systems Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow, Scikit-learn) Hands-on experience building Agentic AI systems (multi-agent frameworks, autonomous workflows, orchestration patterns) Experience with LLM application frameworks such as LangChain, Semantic Kernel, AutoGen, or equivalent Solid understanding of RAG (Retrieval-Augmented Generation), vector databases (FAISS, Pinecone, Azure AI Search) Experience designing prompt engineering strategies, evaluation pipelines, and guardrails Strong skills in API development and microservices architecture (FastAPI, Flask) Familiarity with cloud platforms (Azure preferred) including Azure OpenAI, AI Studio, Functions, Kubernetes Experience with CI/CD, MLOps, and deploying ML/AI solutions at scale Strong problem-solving skills and ability to design end-to-end intelligent systems
Experience: 8+ years total, with deep exposure to enterprise AI use cases Experience building multi-agent autonomous systems with reasoning, planning, and tool use Knowledge of graph-based workflows and orchestration frameworks Experience in fine-tuning LLMs and working with open-source models (Llama, Mistral, etc.) Familiarity with evaluation frameworks (prompt testing, hallucination detection, safety benchmarking) Experience with real-time streaming data systems and event-driven architectures Exposure to Responsible AI practices, governance, and model risk management Experience in data engineering pipelines (Spark, Databricks) is a plus Strong collaboration and stakeholder management skills in enterprise environments
AI Skills Expectation:\\\\r\\\\nAll contractor resources are expected to demonstrate baseline proficiency in enterprise-approved AI tools as part of their day-to-day responsibilities:\\\\r\\\\n\\\\r\\\\nConsistent Use:\\\\r\\\\nMaintain a minimum of 90% weekly usage of AI tools such as GitHub Copilot, Microsoft 365 Copilot, and other approved GenAI platforms.\\\\r\\\\n\\\\r\\\\nApplied Productivity\\\\r\\\\nLeverage AI tools to enhance\\\\r\\\\n\\\\r\\\\nCode generation and debugging\\\\r\\\\nDocumentation and technical writing\\\\r\\\\nData analysis and insights generation\\\\r\\\\nDecision-making workflows\\\\r\\\\n\\\\r\\\\nContinuous Learning:\\\\r\\\\nStay current with evolving AI capabilities and apply new features, tools, and patterns to improve delivery quality, speed, and innovation.
Key Responsibilities
Skill Requirements
Design and develop Agentic AI systems that can autonomously perform complex workflows using LLMs and tools Build and optimize RAG pipelines, multi-agent orchestration, and decision-making frameworks Develop scalable APIs and services to integrate AI capabilities into enterprise applications Implement evaluation, monitoring, and guardrails to ensure reliability, safety, and performance of AI systems Collaborate with cross-functional teams to translate business problems into AI-driven solutions
Other Requirements
Experience in enterprise AI transformation initiatives Domain exposure to Healthcare is a plus Experience building automation platforms, copilots, or AI assistants Background in data science, applied ML, or distributed systems engineering Experience working in Agile product teams delivering AI-powered applications