Roles & Responsibilities

Role Overview

We are looking for an experienced Generative AI / LLM Engineer to design, develop, and deploy AI-powered applications and solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, and modern AI frameworks.

The ideal candidate will have strong hands-on experience in Python, LLMs, RAG, prompt engineering, AI application development, APIs, and cloud-based AI platforms. Experience with production-grade GenAI solutions and enterprise applications will be highly preferred.

Key Responsibilities

• Design and develop enterprise-grade Generative AI and LLM-based applications.
• Build and deploy RAG-based solutions using enterprise and unstructured data.
• Develop AI agents and multi-step AI workflows for business use cases.
• Integrate LLMs with enterprise applications, APIs, databases, and external services.
• Work with models such as OpenAI, Azure OpenAI, Llama, Mistral, Gemini, or equivalent.
• Develop prompt engineering strategies and optimize model responses.
• Implement vector search and semantic retrieval using vector databases.
• Develop APIs and backend services for AI applications.
• Evaluate LLM performance, accuracy, hallucination, latency, and cost.
• Implement techniques for improving response quality and context retrieval.
• Collaborate with Data Scientists, Data Engineers, Software Engineers, Architects, and business stakeholders.
• Deploy AI solutions on cloud platforms and support production environments.
• Implement monitoring, logging, security, and governance for AI applications.
• Stay updated with emerging developments in GenAI, Agentic AI, LLMs, and AI engineering.

Mandatory Skills

• Strong programming experience in Python.
• Hands-on experience with Generative AI / LLM applications.
• Strong understanding of Large Language Models (LLMs).
• Experience building RAG pipelines.
• Experience with Prompt Engineering.
• Experience working with LLM APIs such as OpenAI/Azure OpenAI or equivalent.
• Experience with AI/ML frameworks and libraries.
• Strong knowledge of REST APIs and backend application development.
• Experience with vector databases such as Pinecone, FAISS, Weaviate, Milvus, or Chroma.
• Strong understanding of embeddings, semantic search, context retrieval, and tokenization.
• Good knowledge of SQL and databases.
• Experience deploying applications on AWS, Azure, or GCP.

Good-to-Have Skills

• Experience with Agentic AI and autonomous AI workflows.
• Experience with LangChain / LangGraph / LlamaIndex.
• Knowledge of Model Context Protocol (MCP).
• Experience with Azure OpenAI and Azure AI services.
• Experience with Databricks.
• Knowledge of MLOps / LLMOps.
• Experience with Docker and Kubernetes.
• Knowledge of CI/CD pipelines.
• Experience with model evaluation frameworks.
• Knowledge of AI security, responsible AI, and data privacy.
• Experience integrating GenAI with enterprise systems such as CRM, ERP, ITSM, or workflow platforms.

Preferred Technology Stack

Languages: Python, SQL

AI/LLM: OpenAI, Azure OpenAI, Llama, Mistral, Gemini

Frameworks: LangChain, LangGraph, LlamaIndex

Vector Databases: Pinecone, FAISS, Weaviate, Milvus, Chroma

Cloud: AWS / Azure / GCP

DevOps: Docker, Kubernetes, Git, CI/CD

Data: Databricks, Spark, SQL