Generative AI- Data Scientist-Senior Associate
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Generative AI- Data Scientist-Senior Associate
Location
Bangalore
Experience
Senior
Posted
Jul 10, 2026
Apply by
August 9, 2026
Applicants
0
Early applicantFull-timeWork from Office
Job Description
**Industry/Sector**
Not Applicable
**Specialism**
Data, Analytics & AI
**Management Level**
Senior Associate
**Job Description & Summary**
The Opportunity
When you join PwC Acceleration Centers (ACs), you step into a pivotal role focused on actively supporting various Acceleration Center services, from Advisory to Assurance, Tax and Business Services. In our innovative hubs, you’ll engage in challenging projects and provide distinctive services to support client engagements through enhanced quality and innovation. You’ll also participate in dynamic and digitally enabled training that is designed to grow your technical and professional skills.
As part of the Data and Analytics team you will develop and implement innovative machine learning models for GenAI projects. As a Senior Associate, you will analyze complex problems, mentor junior team members, and build meaningful client relationships while navigating the evolving landscape of data science. Join us to leverage your technical knowledge and contribute to groundbreaking solutions that shape the future of AI.
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.
**Travel Requirements**
Not Specified
**Job Posting End Date**
Key Responsibilities
- Develop and implement agentic AI systems including tool-calling, autonomous task planning, and multi-step reasoning workflows.
- Deploy agentic AI solutions using cloud-native services such as Azure AI Foundry, AWS Bedrock Agents, or GCP Vertex AI Agent Builder.
- Implement and maintain evaluation frameworks for agentic AI solutions to assess reasoning, reliability, and hallucination risk.
- Design, develop, and optimize RAG pipelines end-to-end using LangChain, LlamaIndex, and production-grade vector databases.
- Design and optimize prompt engineering strategies including prompt chaining and few-shot/zero-shot techniques.
- Collaborate with cross-functional teams to translate business requirements into technical implementations.
- Implement and uphold Python development best practices during implementation.
- Monitor solution performance, track key metrics, and proactively adjust implementations to improve accuracy and latency.
- Communicate technical findings, implementation progress, and insights to stakeholders.
Requirements
- Bachelor's Degree
Skills Required
PythonLangChainLangGraphCrewAIAutoGenAzure AI FoundryAWS Bedrock AgentsGCP Vertex AI Agent BuilderRAGLlamaIndexPineconeWeaviateOpenSearchAzure AI SearchPrompt EngineeringCI/CDOral and written proficiency in EnglishMentoringCollaborationCommunicationMCPA2AGraph RAGVectorless RAGHybrid RAGModel buildingFine-tuningQuantizationFeature engineeringModel evaluationResponsible AI principlesSecurity practices
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