Senior Manager (11003)

Position Summary

We are seeking a skilled and experienced AI Engineer to design, build, and operate production-grade generative AI capabilities. This role focuses on developing advanced Retrieval-Augmented Generation (RAG) pipelines, sophisticated multi-agent systems, and robust automated evaluation frameworks. The ideal candidate will bridge the gap between applied data science and rigorous software engineering, focusing on building scalable, secure, and cost-efficient AI-powered products.

Job Responsibilities

• GenAI Application Architecture:Architect and deploy production-grade LLM applications using microservices (e.g., FastAPI) and robust software engineering practices, including APIs, integration testing, and CI/CD.
• Advanced RAG Engineering:Build and optimize end-to-end RAG pipelines, including document ingestion, semantic chunking strategies, metadata enrichment, vector database indexing (e.g., Azure AI Search), hybrid search (e.g., BM25 + vectors), and reranking to ground model outputs and minimize hallucinations.
• Agentic AI Workflows:Design and implement agentic AI solutions, incorporating tool-calling, state management, memory architectures, planning vs. reacting agent design, reflection loops, and multi-agent coordination using frameworks like LangGraph and AutoGen. Develop human-in-the-loop systems for verification and control.
• LLMOps & Lifecycle Management:Establish and manage operational standards for the AI lifecycle, including model fine-tuning, prompt versioning, semantic caching, rate limiting, and dynamic model routing to optimize for latency, cost (token economy), and performance. Implement CI/CD pipelines for AI/ML workloads.
• Model Evaluation & Observability:Develop and implement automated evaluation loops using LLM-as-a-judge methodologies to assess faithfulness, relevance, and toxicity. Monitor model drift, performance, and reliability using frameworks such as RAGAS, TruLens, and DeepEval.
• AI Safety & Governance:Implement strict guardrails (e.g., NeMo Guardrails, Llama Guard) to protect against prompt injection, data leakage, and other vulnerabilities, ensuring alignment with enterprise Responsible AI standards.

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