Director Data Scientist (GenAI) and Agentic Engineering at Blend360

We are seeking GenAI and Agentic AI Engineering Hands-on Leader with a focus on delivery, client excellence and innovation. Experienced Agentic AI Engineer Leader with deep expertise in LLM, Azure AI, Snowflake, and Machine Learning ecosystems to design and implement enterprise-grade AI solutions. The ideal candidate combines strong technical leadership with hands-on experience architecting end-to-end AI/ML systems—from data readiness pipeline through Agentic Solutions deployment— leveraging cloud-native architecture. Test Driven Agentic AI Engineering, evaluation strategy, metric selection, ground-truth creation, and decisioning on model and prompting approaches. You’ll build and validate GenAI/agentic solutions, define what “good” means, and ensure solutions are measurably effective and safe before and after launch. You will build the GenAI MVP solution in a production-intent way (model choice, RAG/agent behaviour, prompts, and evaluation) Key Responsibilities • Lead P&L and revenue generation by developing solutions and leading Agentic Initiatives. Person should be hands-on in terms of code development. • Translate business needs into testable GenAI and Agentic Engineering solutions, clear outputs, and measurable success criteria; define scope boundaries (what the system should not attempt), including risks. • Run feasibility assessments to choose the right approach: prompting vs RAG vs fine-tuning vs classical ML. • Select and develop models based on task requirements (reasoning vs extraction vs classification) working with AI Engineering to understand latency/cost, and risk profile. • Design prompting strategies: instruction design, few-shot sets, structured outputs, tool/agent prompts, and robustness patterns. This will be implemented as an MVP and iterate based on eval results. • Establish prompt iteration methodology driven by evals (not anecdotal testing): prompt versioning, ablations, and change control. • Define the evaluation plan for GenAI systems and agentic workflows- designing and implementing evaluation from LLM as a judge and ensure evaluation includes fairness and bias considerations where applicable. Define acceptance thresholds and release gates tied to these metrics. • Own experimentation and model improvements: Run structured experiments (across prompts, retrievers, chunking, models). • Develop out methods for identifying model failures such as hallucination types, retrieval misses, instruction-following errors, formatting failures etc • Provide recommendations for improvements grounded in evidence: what to change, expected lift, and trade-offs. • Deliver an engineering-ready handoff: prompt packages and versioning approach, RAG configuration, tool schemas (if agentic), evaluation harness, datasets/ground truth, metric definitions, and go/no-go gates. • Design scalable and secure Agentic AI architectures adhering to best practices in data engineering, MLOps and LLMOps.