Director Data Scientist (GenAI) and Agentic Engineering

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 systemsfrom 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. Youll 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.

Qualifications :

• 15-17 years of overall AI/ML experience out if which at least 4 years of Generative AI solutions.
• Strong background in applied ML data science LLM and Agentic AI Engineering Systems with demonstrated delivery and client facing experience.
• Deep expertise in evaluation design metrics and dataset curation for LLM systems.
• Proven experience in model selection and prompt engineering including structured output and tool-use prompting.
• Strong proficiency in Python and major ML frameworks (PyTorch TensorFlow Scikit-learn).
• Strong experience in LLM fine-tuning RAG Context Engineering Claude Code Open AI Codex Agentic Workflows.
• Strong RAG design choices (chunking embeddings retrieval strategies reranking) and how to evaluate them.
• Must have implemented Agentic AI SDLC
• Working with GenAI on Azure AWS or Snowflake involves leveraging cloud-native AI toolssuch as Azure OpenAI AWS Bedrock or Snowflake Cortexto build or consume intelligent solutions directly on governed data.
• Experience on vibe coding - such as AntiGravity Cursor and VS Code is highly desirable.
• Proven ability to build end-to-end GenAI MVPs in Python (RAG/agents evaluation harness) and prepare them for production handoff.
• Excellent communication and stakeholder management skills with a strategic mindset.

Required Collaboration Model

• Partner AI engineering for LLM implementation needs by providing clear specs (prompts/tool schemas) eval harnesses and acceptance thresholds.
• Mentor DS/analysts on GenAI evaluation methods labelling operations and scientific rigor.
• With Product and Software Engineers for integrating AI capabilities into platforms and user-facing services.
• With DevOps/Platform Engineers for environment setup monitoring infrastructure and reliability.
• With Data Engineering for designing and accessing upstream data pipelines.

Additional Information :

Why Blend360

• Impactful Technical Work: Be at the forefront of AI innovation designing and implementing cutting-edge technical solutions for leading companies and making a tangible impact on their businesses.
• Growth Opportunities: Thrive in a company and innovative team committed to growth providing a platform for your technical and professional development.
• Collaborative Culture: Work alongside a team of world-class experts in data science AI and technology fostering an environment of learning sharing and mutual support on complex technical challenges.
• Bold Vision: Join a company that is brave goes the extra mile to innovate and delivers bold visions for the future of AI.
• If you are a visionary & passionate about leveraging AI and GenAI to drive business transformation and are excited by the prospect of shaping the future of our clients we encourage you to apply!

Remote Work :

No

Employment Type :

Full-time