Generative AI Developer - Senior Associate

Experience

Role Overview

Key Responsibilities

• LLM & GenAI Development: Design, develop, and maintain LLM-powered applications using APIs such as OpenAI, Azure OpenAI, Anthropic, or Cohere. Implement core GenAI workflows including summarization, classification, Q&A, and content generation.
• Agentic AI Implementation: Build and orchestrate multi-agent systems using frameworks like CrewAI, LangGraph, OpenAI Agents SDK, or Azure AI Agent Framework . Implement inter-agent communication, task routing, and tool orchestration for complex workflows.
• RAG & Embedding Pipelines: Implement retrieval-augmented generation pipelines involving embedding creation, chunking, and vector indexing using FAISS, Pinecone, Chroma, or Milvus . Ensure efficient retrieval for context-aware outputs.
• Prompt Engineering & Techniques: Apply advanced prompting strategies such as Few-Shot Learning, Chain-of-Thought (CoT), ReAct, and CART to improve reliability and interpretability of model responses.
• Testing & Validation: Establish automated and manual testing pipelines using TruLens , LangSmith , PromptLayer , or DeepEval to evaluate model quality, accuracy, safety, and factual grounding. Define and track metrics such as coherence, hallucination rate, and response diversity.
• Performance Monitoring & Optimization: Implement cost-effective token management and model observability using logging and tracing frameworks. Continuously optimize prompts, retrieval logic, and memory mechanisms to improve efficiency.
• Data Analysis & Preparation: Conduct EDA and pre-processing of textual datasets to ensure quality input for training, evaluation, and fine-tuning tasks.
• Documentation & Collaboration: Create detailed design documents, maintain experiment logs, and collaborate with architects, data scientists, UI/UX engineers, and product managers to ensure solution alignment with business goals.
• Continuous Learning: Stay up to date with new model releases (OpenAI GPT, Claude, Gemini, Mistral, LLaMA, etc.) and frameworks in the GenAI and agentic AI ecosystem.

Key Skills & Competencies

• Strong proficiency in Python , LangChain , LlamaIndex , CrewAI , and LangGraph
• Deep understanding of LLM architectures , tokenization , embedding models , and vector databases
• Experience with prompt testing and evaluation frameworks (TruLens, LangSmith, PromptLayer, DeepEval)
• Familiarity with bias mitigation , safety testing , and Responsible AI principles
• Knowledge of MLOps/LLMOps practices for deploying and monitoring AI models
• Strong analytical mindset, documentation discipline, and collaborative approach
• Exposure to at least one major cloud AI platform ( Azure , AWS , GCP , or OCI )

Preferred Background