Lead Data Scientist - GenAI at Blend360

Hyderabad, Telangana, India -

Full Time

Start Date

Immediate

Expiry Date

14 May, 26

Salary

0.0

Posted On

13 Feb, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Generative AI, Data Science, Problem Framing, Experimental Design, Evaluation Strategy, Prompting, RAG, Agentic Workflows, Model Selection, Fine-Tuning, LLM As A Judge, Python, PyTorch, TensorFlow, Scikit-learn, Azure GenAI

Industry

Professional Services

Description

Company Description Blend is a premier AI services provider, committed to co-creating meaningful impact for its clients through the power of data science, AI, technology, and people. With a mission to fuel bold visions, Blend tackles significant challenges by seamlessly aligning human expertise with artificial intelligence. The company is dedicated to unlocking value and fostering innovation for its clients by harnessing world-class people and data-driven strategy. We believe that the power of people and AI can have a meaningful impact on your world, creating more fulfilling work and projects for our people and clients. For more information, visit www.blend360.com Job Description Own the scientific and methodological side of GenAI delivery: problem framing, feasibility assessment, experimental design, evaluation strategy, metric selection, ground-truth creation, and decisioning on model and prompting approaches. You’ll build and validate GenAI/agentic prototypes, 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). AI Engineering will lead the overall design and will partner with you to harden, optimise, integrate, and scale the MVP into an enterprise-grade service. Key Responsibilities: Translate business needs into testable GenAI hypotheses, 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, thresholds and metric creation i.e. recall@k, precision@k. Ensure evaluation includes fairness and bias considerations where applicable. Define acceptance thresholds and release (“go/no-go”) 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 tradeoffs. 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. Required Collaboration Model: Act as the GenAI DS lead in project delivery: align stakeholders on success metrics, evaluation readouts, and go/no-go decisions. 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. Qualifications 7 + years of overall AI/ML experience including 2+ years of Generative AI solutions Strong background in applied ML / data science with demonstrated GenAI delivery 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). Experience in LLM fine-tuning, prompt engineering, or AI solution integration with enterprise applications. Familiarity with RAG design choices (chunking, embeddings, retrieval strategies, reranking) and how to evaluate them. Comfortable working with Azure GenAI ecosystem (Azure OpenAI / Azure AI Foundry) from a consumer/solution perspective. 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. 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!

Responsibilities

The Lead Data Scientist will own the scientific and methodological aspects of GenAI delivery, including problem framing, experimental design, evaluation strategy, and defining model/prompting approaches. This role involves building and validating GenAI prototypes to ensure measurable effectiveness and safety before and after launch.