Required Skills and Experience

AI AND LLM ENGINEERING, ESSENTIAL
4+ years building production LLM integrated applications, not prototypes or notebooks, but shipped systems handling real data
Deep expertise in agentic AI patterns, including ReAct loops, tool use, multi agent orchestration, agent memory, and state management
Hands on prompt engineering at production scale, including system prompt design, few shot construction, chain of thought reasoning, structured output enforcement, and adversarial or critic prompt patterns
RAG implementation experience, including embedding models, vector databases (pgvector, Pinecone, Weaviate, or equivalent), semantic chunking, and retrieval evaluation
Proficiency with Azure OpenAI SDK and or Vertex AI SDK, covering model invocation, streaming, error handling, retry logic, and token management
Understanding of LLM failure modes such as hallucination, confidence calibration, context window limits, and prompt injection, along with architectural mitigations for each

What You Will Build

CORE AGENTIC PIPELINE
Design and implement multi agent orchestration pipelines, coordinating sequences of specialised AI agents across a structured, multi stage workflow
Implement Human in the Loop (HITL)



workflow engine, stateful approval gates where named human reviewers must approve, edit, or reject AI outputs before pipeline advancement
Ensure all agents are stateless between sessions, no cross session memory accumulation, no passive learning from runtime data

LLM INTEGRATION AND PROMPT ENGINEERING
Build and maintain the Model Abstraction Layer, decoupling agent logic from specific model versions to enable zero downtime model upgrades
Engineer sophisticated multi turn system prompts encoding domain knowledge, evaluation criteria, output structure requirements, and adversarial review logic
Implement Retrieval Augmented Generation (RAG) pipelines using embedding models for semantic document retrieval, covering context window management, chunking strategy, and retrieval precision optimisation
Manage model inference parameters (temperature, top P, top K, max tokens) per agent role, balancing output consistency and quality
Integrate with Azure OpenAI (GPT 5.5, GPT 5.4, text embedding 3 large) and or Vertex AI (Gemini 2.0 Pro, Gemini 2.0 Flash, text embedding gecko) via managed API endpoints.

📌 Ai Developer Noida
🏢 NLB Services
📍 Noida