Job Description

Experience: 2+ Years

About the Role

A mid-level developer who is passionate about Generative AI and wants to work on production-grade LLM applications using Python, Azure OpenAI, and modern frameworks like

LangChain and LlamaIndex.

You’ll build intelligent systems—from RAG pipelines and conversational agents to agentic AI workflows—that bring reasoning and automation to our customer-facing and internal tools.

Key Responsibilities

• Design, build, and deploy LLM-based applications using Python and Azure OpenAI Services.

• Develop Retrieval-Augmented Generation (RAG) pipelines with vector databases like Azure Cognitive Search, Pinecone, or FAISS.

• Create intelligent agents using frameworks such as LangChain, LlamaIndex, or Semantic Kernel.

• Apply advanced prompting techniques, including:

• ReAct, Self-Consistency, and Toolformer-style prompting

• Implement reasoning strategies for multi-step decision-making and task decomposition.

• Orchestrate agentic AI workflows, integrating external tools, APIs, and business logic.

• Deploy scalable LLM services with Azure Functions, Docker, and serverless architectures.

• Monitor and optimize model performance, latency, and reliability.

Must-Have Skills

• 2 – 4 years of hands-on Python development experience.

• Proven integration of Azure OpenAI (GPT models) into real-world applications.

• Strong understanding of LLMs, embeddings, vector search, and RAG architectures.

• Experience with at least one LLM orchestration framework: LangChain, LlamaIndex, or Semantic Kernel.

• Knowledge of structured prompting techniques and LLM reasoning models.

• Ability to integrate external APIs, cloud tools, and business data into LLM pipelines.

• Familiarity with Azure Functions, Docker, and cloud-native development.

• Excellent debugging and performance optimization skills.

Good-to-Have Skills

• Experience with multi-agent systems, agentic reasoning, or cognitive architectures.

• Background in building AI developer tools, automation bots, or assistants.

• Exposure to LLMOps practices: versioning, prompt evaluation, monitoring.

• Familiarity with traditional NLP tasks: summarization, classification, Q&A.

• Experimental prompting experience: role prompting, reflection-based refinement, zero-shot CoT, etc.

Employment Status

Full Time

Educational Requirements

B.Tech

Experience Requirements

1 - <3 Years