Data Scientists

4 days ago

Bengaluru, Karnataka, India Skill On Tap Full time ₹ 8,00,000 - ₹ 24,00,000 per year

Job Title: Data Scientists (GenAI/Agentic)

Experience: 3 to 5 years

Location: Bangalore

Work Hours: 12:30 PM till 9:30 PM; (~2-3 hours overlap to US Central Time)

Work Arrangement: Hybrid (Monday, Tuesday and Wednesday work from office, Thursday and Friday work remotely)

Client is in direction to implement full 5 days working from office. Resource need to be ready to comply the same.

Team Model: Collaborates with AI Engineers (Python/FastAPI/LangGraph Studio), Frontend Developers, Platform/Data Engineers, and a UI/UX Designer

Job Description

We are seeking a highly skilled and collaborative team to Design, build, and productionize GenAI/Agentic systems and end-to-end applications. Rapidly prototype (using Windsurf/Copilot) and then harden into reliable, observable, secure services on Azure.

Role Summary:

Own agentic workflows (LangGraph), MCP tools, retrieval/embedding pipelines, and model evaluation.

Translate ambiguous problems into robust, measurable solutions with clear documentation and business impact.

Key Responsibilities

  • Build agentic workflows in LangGraph; create reusable templates for multi-tool agents.
  • Design, implement, and operate MCP servers/tools to expose APIs, data access, and actions.
  • Apply advanced prompt engineering; maintain a versioned prompt registry with telemetry and A/B tests.
  • Build embedding pipelines for semantic search/classification/clustering/retrieval; integrate with downstream apps (intent detection, topic modelling, deduplication, ranking).
  • Apply dimensionality reduction and similarity search (PCA/t-SNE/UMAP; cosine/Euclidean; FAISS/ScaNN).
  • Build and evaluate ML models (regression, random forest, XGBoost/LightGBM, SVM, Naive Bayes, k-means, hierarchical clustering) with sound diagnostics and inference.
  • Design experiments (A/B/MVT/DOE) and causal analyses (PSM, causal forests, DiD); translate into actionable insights.
  • Partner with Full Stack and DevOps on data contracts, latency/SLOs, observability, and deployment.

Required Technical Skills

  • Python (advanced): FastAPI, async I/O, packaging, pytest; Linux proficiency
  • GenAI/Agentic: LangGraph (templates/orchestration), MCP servers/tools, LLM tool-use/function calling, retrieval, streaming
  • LLM Fine-tuning & Eval: SFT/ORPO/DPO; prompt/response evaluation frameworks; guardrails
  • Embeddings & Vector DBs: TF-IDF, Word2Vec, GloVe, FastText, BERT/SBERT, OpenAI/Azure OpenAI; FAISS, Azure AI Search, Pinecone, ScaNN
  • Statistics & Causal: hypothesis testing, CIs, bootstrapping, Bayesian basics; feature selection (Lasso/Ridge, RFE); SHAP
  • Data Eng basics: PySpark, SQL; Azure Databricks, Data Factory, Data Lake; MongoDB/Cosmos DB
  • Observability: MLflow, Application Insights; model drift detection

Nice-to-Have

  • Prompt linting/eval frameworks; graph analytics; vector augmentation; prompt caching
  • Visualization (Plotly, Seaborn) and stakeholder-ready reporting

Success Metrics

  • 95% agentic workflow success rate; reliable fallbacks
  • Improved retrieval metrics (NDCG/MRR/)
  • Validated experiments with adopted insights
  • Clear, reproducible notebooks/pipelines; green MLOps checks

Tools/Stack you will use :

  • Languages/Frameworks: Python, FastAPI
  • Agent Orchestration: LangGraph, MCP (custom servers/tools)
  • APIs & Communication: REST, WebSockets,
  • LLM Providers: OpenAI, Azure OpenAI
  • Libraries: scikit-learn, XGBoost, LightGBM, SHAP, Pandas, NumPy, Matplotlib, Seaborn, Plotly and many others that project requires.
  • Coding Assistance: Windsurf, GitHub Copilot

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