Data Scientists (GenAI/Agentic)

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/Recall@K)
• 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