Lead Data Scientist GenAI

Hyderabad - India

Monthly Salary: Not Disclosed

Posted on: 13 hours ago

Vacancies: 1 Vacancy

Job Summary

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. Youll 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. 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!

Remote Work :

Employment Type :

Full-time

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. 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!

Remote Work :

Employment Type :

Full-time

Key Skills

Laboratory Experience
Immunoassays
Machine Learning
Biochemistry
Assays
Research Experience
Spectroscopy
Research & Development
cGMP
Cell Culture
Molecular Biology
Data Analysis Skills

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About Company

Blend360

Blend360 is an award-winning provider of data, analytics, and talent solutions for Fortune 500 companies. The company has made the Inc. 5000 list of Fastest Growing Companies every year they have been in business and has been awarded a world-class ranking in client satisfaction for th ... View more

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