AI Specialization

2+ years specifically building with LLMs, with deep familiarity in:

1. Orchestration: LangChain, LangGraph, or similar state-based frameworks.
2. Vector DBs: Pinecone, Weaviate, or pgvector.
3. Prompt Engineering: Advanced techniques (Chain-of-Thought, ReAct, Few-Shot).

• Production Mindset: Experience not just building demos, but operating them. You know how to handle rate limits, context window overflows, and non-deterministic errors.
• Soft Skills: Ability to explain “probabilistic software” to non-technical stakeholders—managing expectations that agents are never 100% accurate, but can be 100% useful.
• Communication: Excellent communication skills, with experience in documenting technical designs, sharing insights, and enabling team knowledge transfer.

Responsibilities:

Agentic Engineering & Orchestration

• Workflow Design: Architect complex, multi-agent workflows using Microsoft AI tech stack. Design, Develop and Deploy agents to handle loops, interruptions, and human-in-the-loop interventions.
• Tool Use & Function Calling: Build reliable “tool layers” that allow LLMs to safely interact with internal APIs, databases, and third-party SaaS platforms (e.g., SalesForce, Workday, ServiceNow etc.)
• State Management: Design persistence layers to manage agent memory, conversational history, and context windows efficiently.

Advanced Data & RAG Strategy

• Retrieval Pipelines: Build production-grade data retrieval and integration systems. Optimize vector indexing, document chunking, and re-ranking algorithms to ensure high-precision context retrieval.
• Data Quality: Collaborate with Data Engineers to curate “Golden Datasets” for agent consumption

LLMOps, Evaluation & Quality

• Automated Evaluation: Build CI/CD pipelines for AI that include “LLM-as-a-Judge” testing. Leverage frameworks to score agent outputs for accuracy, hallucination, and safety before deployment.
• Observability: Instrument applications with tracing tools to visualize agent reasoning chains, monitor latency, and debug failures in production.
• Cost Optimization: Monitor token usage and latency, optimizing prompt density and caching strategies to maintain high performance at sustainable costs.

Innovation & Collaboration

• Prototyping to Production: Rapidly validate new ideas using state-of-the-art models, then refactor successful prototypes into maintainable, tested production code.
• Standards Adoption: Stay ahead of the curve by evaluating emerging technologies to standardize agent connectivity.