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