Leading the design and development of advanced data solutions to transform raw data into actionable insights
Guiding teams in leveraging advanced analytics and statistical techniques for data-driven decision making
Utilizing machine learning and artificial intelligence to enhance data science workflows and predictive modeling
Developing and implementing data pipelines and data lakes to support complex data analysis
Overseeing the creation of data visualizations to solve complex business problems and inform strategic decisions
Collaborating with client teams to identify opportunities for data-driven growth and innovation
Mentoring team members to develop their skills in data science and analytics
Validating data quality and integrity within analytics frameworks
Encouraging the adoption of creative technologies and leading practices in data engineering
Addressing and resolving conflicts or issues with clients and team members to maintain project timelines and deliverables

What You Must Have
At least a Bachelor's & Master's degree
At least 8 + years of experience
Oral and written proficiency in English required

What Sets You Apart
Over 3 years of experience in developing and scaling Generative AI projects from prototypes to enterprise production, managing throughput, latency, cost,



and multi-region deployments.
Proven expertise implementing AI interoperability protocols like MCP (Model Context Protocol) and A2A (Agent-to Agent) at scale for seamless system integration.
Skilled in using enterprise cloud AI platforms such as Azure AI Foundry, Amazon Bedrock, and Google Vertex AI to build and deploy production-grade agentic AI solutions.
Advanced Python programming skills and hands-on experience with agentic AI frameworks including LangChain, LangGraph, CrewAI, and AutoGen for building robust generative AI applications.
Deep understanding of advanced Retrieval
Augmented Generation (RAG) architectures (Graph RAG, Vectorless RAG, Hybrid RAG) and traditional AI/ML fundamentals like model building, fine-tuning, and evaluation.
Solid knowledge of LLM security risksprompt injection, jailbreaking, data exfiltration, tool misuse—and experience designing defense-in-depth safeguards within agentic system architectures.
Expertise in containerization and cloud-native orchestration (Kubernetes, Docker, serverless) and event-driven architectures for scalable deployment of agentic AI workloads; holds relevant AI or solution architecture certifications.

📌 Gen Ai Architect Lead Data Scientist Bengaluru
🏢 PwC
📍 Bengaluru