Data Scientist

2 days ago

Hyderabad, Telangana, India Jaggaer Full time ₹ 15,00,000 - ₹ 25,00,000 per year

Overview:

Data Scientist (Generative AI & Agents)

Role overview

We're looking for a data scientist with strong analytical and machine learning foundations who is eager to grow into the world of generative AI and autonomous agents. You'll apply statistical rigor and ML skills to solve real-world problems, while contributing to the design and evaluation of agentic workflows powered by the latest frameworks.

Principal Responsibilities:

What you'll do

  • Apply core DS/ML: Design experiments, build predictive models, and apply statistical methods for causal inference, forecasting, and optimization.
  • Work with GenAI models: Fine-tune and evaluate LLMs for retrieval-augmented generation (RAG), classification, and summarization tasks.
  • Contribute to agentic systems: Assist in developing multi-agent or tool-using workflows using frameworks like LangChain, LangGraph, and ADK.
  • Data engineering: Prepare large datasets, ensure data quality, and build pipelines for both structured and unstructured data.
  • Evaluation & safety: Help design metrics, test harnesses, and guardrails for generative AI systems.
  • Collaborate cross-functionally: Work closely with senior engineers and product teams to translate business needs into experiments and prototypes

Position Requirements:

  • 3–4 years of experience in data science, applied ML, or analytics.
  • Strong foundations in statistics, experimental design, and optimization.
  • Proficiency in Python (pandas, scikit-learn, PyTorch/TensorFlow, or equivalent).
  • Hands-on experience with at least one cloud platform (AWS, GCP, or Azure).
  • Familiarity with GenAI concepts: LLMs, embeddings, and vector databases.

Nice to Have:

  • Exposure to agent frameworks (LangChain, LangGraph, ADK).
  • Experience with MLOps and observability tools (MLflow, W&B, langsmith) and deployment workflows.
  • Knowledge of information retrieval or RAG pipelines.
  • Interest in model evaluation, fairness, privacy, and safety in AI.

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