Generative AI Data Scientist Senior Associate

About the job

Responsibilities • Develop and implement agentic AI systems — including tool-calling, autonomous task planning and execution, multi-step reasoning workflows, agent memory management, and human-in-the-loop escalation mechanisms for complex enterprise use cases. Leverage Agentic frameworks like LangGraph, CrewAI, Autogen, cloud-based frameworks, etc. • Develop and deploy agentic AI solutions using any of the cloud-native services as per defined requirement (Azure AI Foundry, AWS Bedrock Agents, GCP Vertex AI Agent Builder, etc.), and associated cloud services for scalable agent execution. • Implement and maintain evaluation frameworks for agentic AI solutions — assessing agent reasoning, tool-use reliability, multi-step task completion, hallucination risk, and autonomous decision quality to ensure production readiness and continuous performance improvement. • Design, develop, and optimize RAG pipelines end-to-end (using LangChain, LlamaIndex, etc.) — including chunking strategies, embedding model selection, integrate production-grade vector databases (Pinecone, Weaviate, OpenSearch, Azure AI Search, etc.), hybrid retrieval, re-ranking. • Design, develop, and optimize prompt engineering strategies, including prompt chaining, few-shot/zero-shot techniques, and prompt templating, to enhance the accuracy, reliability, and consistency of LLM-powered applications and agentic workflows. • Collaborate with cross-functional teams (client managers, data scientists, architects, DevOps engineers) to translate business requirements into technical implementations — ensuring alignment with solution architecture defined by the lead architects. • Implement and uphold Python development best practices during implementation. • Monitor solution performance, track key metrics, test all affected scenarios and proactively adjust implementations — identifying issues in accuracy, latency, throughput, and cost, and applying optimizations at the code and infrastructure level. • Communicate technical findings, implementation progress, and insights to stakeholders — contributing to documentation, sprint demos, and knowledge-sharing within the team. What You Must Have • Bachelor's Degree • 4 years of experience • Oral and written proficiency in English required. What Sets You Apart • Proven experience building and deploying production-grade Agentic AI solutions using frameworks like LangChain, LangGraph, CrewAI, or AutoGen. • Strong knowledge of AI interoperability protocols (e.g., MCP, A2A) and advanced Retrieval-Augmented Generation (RAG) architectures such as Graph RAG, Vectorless RAG, and Hybrid RAG. • Deep understanding of traditional AI/ML fundamentals including model building, fine-tuning, quantization, feature engineering, and model evaluation. • Foundational awareness of Responsible AI principles, security practices for safe deployments, and experience evaluating GenAI and Agentic AI applications. • Hands-on experience with at least one major cloud provider (AWS, Azure, or GCP), familiarity with CI/CD pipelines, and relevant AI/GenAI certifications preferred.

Requirements

• Machine Learning
• GenAI
• AI Systems
• RAG
• Prompt Engineering
• Python

Qualifications

• Bachelor's Degree
• 4 years of experience

Preferred Technologies

• Machine Learning
• GenAI
• AI Systems
• RAG
• Prompt Engineering
• Python