Role Summary

We are seeking a Senior Data Scientist with strong hands-on experience across Traditional AI/ML and Generative AI (GenAI). This role will primarily focus on one project at a time, owning core experimentation and implementation workstreams, while also guiding and grooming junior data scientists to ensure consistent, high-quality delivery. You'll work closely with the Lead Data Scientist, AI Engineers, Data Engineers, and MLOps to build scalable, production-ready AI capabilities and support client-facing discussions and demos as needed.

Key Responsibilities

Model Development Experimentation (Traditional AI + GenAI)

- Execute end-to-end AI/ML experimentation for a single project: problem framing, dataset readiness, feature engineering, model training/tuning, evaluation, and iteration.
- Build and optimize GenAI pipelines using proprietary and open-source models depending on project needs.
- Contribute to LLM-driven feature development with working knowledge of model behavior, embeddings, prompting strategies, context management, and evaluation.

RAG Agentic Implementation

- Implement and improve RAG pipelines (document ingestion, chunking strategies, embeddings, retrieval, reranking, grounding, and evaluation).
- Develop agentic workflows using modern agent frameworks and tool integrations (multi-step execution, tool use, safety/guardrails).
- Assist in defining evaluation methods for GenAI (faithfulness, relevance, hallucination risk, latency, cost).

Engineering Collaboration Production Readiness

- Work with engineering and MLOps teams to transition experiments into production-grade pipelines.




- Contribute to performance tuning, observability, and reliability of AI services (metrics, logging, monitoring, error analysis).
- Ensure implementation follows best practices for maintainability, scalability, and cost efficiency.

Mentorship Team Enablement

- Guide and groom junior data scientists through structured mentorship: task planning, implementation reviews, and feedback.
- Help define reusable assets (notebooks, evaluation templates, baselines, documentation) to improve team velocity and quality.
- Support the Lead Data Scientist in breaking down tasks and estimating effort for your workstream.

Stakeholder Support

- Participate in technical discussions with internal stakeholders and clients; support demos and walkthroughs where applicable.
- Clearly communicate experimental results, trade-offs, and recommendations through structured documentation and presentations.

Required Qualifications

- 5+ years of hands-on experience in data science / machine learning with delivered outcomes.
- Practical experience in both Traditional ML (supervised/unsupervised methods, NLP, time series, recommendations, etc) and GenAI (LLM-based features).




- Hands-on experience building or contributing to RAG systems; familiarity with agentic frameworks is strongly preferred.
- Solid understanding of model evaluation, experimentation rigor, and iterative improvement cycles.
- Ability to independently own tasks within a project and deliver with minimal supervision.
- Experience mentoring or guiding junior team members through reviews and coaching.
- Strong communication and documentation skills.

Preferred Qualifications

- Graduates from Tier 1 / Tier 2 colleges preferred (eg, IITs, NITs, IIITs, BITS, top state universities and reputed private institutions).
- Experience with enterprise-grade requirements: data privacy, security, governance, and compliance.
- Familiarity with open-source and proprietary model ecosystems and selection trade-offs.
- Exposure to MLOps/LLMOps practices (CI/CD, model/prompt versioning, monitoring, A/B testing).

Key Competencies

- Robust hands-on execution and ownership (single-project focus)
- Structured problem-solving and experimentation discipline
- Mentorship mindset (grooming junior DS)
- Cross-functional collaboration with engineering and MLOps
- Clear communication of results, risks, and recommendations

Reporting Collaboration

- Reports to: Lead Data Scientist / AI Product Engineering Lead
- Collaborates with: AI Engineers, Data Engineers, MLOps/DevOps, Product/Program stakeholders, and (as needed) clients

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Sr Data Scientist - GenAI & Traditional AI (Hyderabad)
🏢 Nstarx India
📍 Hyderabad