Data Scientist – Agentic AI

Role Overview We are looking for an experienced Data Scientist – Agentic AI with strong expertise in Python, Machine Learning, Generative AI, Large Language Models (LLMs), RAG and Agentic AI . The ideal candidate should have hands-on experience in developing, fine-tuning, evaluating and deploying machine learning and GenAI solutions. The candidate should be comfortable working with open-source LLMs, LangChain/LangGraph, PySpark and AI observability/tracing frameworks .

The role involves building intelligent AI systems that can reason, use tools, retrieve information and execute multi-step tasks using Agentic AI architectures. Mandatory Technical Skills 1. Data Science & Python 5+ years of experience in Data Science / Machine Learning / AI. Strong programming experience in Python . Strong understanding of data analysis, feature engineering and statistical techniques.

Experience with Python ML and data science libraries such as: NumPy Pandas Scikit-learn Matplotlib / Seaborn Good understanding of data preprocessing, exploratory data analysis and experimentation. 2. Machine Learning & Statistics Strong understanding of Machine Learning fundamentals . Experience with supervised and unsupervised learning techniques. Knowledge of: Regression Classification Clustering Feature Engineering Model Selection Hyperparameter Tuning Cross-validation Strong understanding of Statistics / ML fundamentals .

Ability to interpret model performance and statistical results. 3. Generative AI / LLM Strong hands-on experience with Generative AI and Large Language Models (LLMs) . Understanding of Transformer architecture and modern LLM-based applications. Experience working with commercial or open-source LLMs. Strong understanding of: Prompt Engineering Context Management Embeddings Tokenization LLM inference Hallucination mitigation 4.

RAG – Retrieval Augmented Generation Strong hands-on experience developing RAG applications . Experience with: Document ingestion Chunking Embeddings Vector search Semantic search Retrieval pipelines Context retrieval Reranking Ability to optimize RAG pipelines for relevance, accuracy and latency. Experience integrating LLMs with enterprise knowledge sources. 5. Agentic AI Hands-on experience building Agentic AI / AI Agent solutions .

Understanding of agent architecture and multi-step reasoning workflows. Experience with: AI Agents Multi-Agent systems Tool Calling Function Calling Agent orchestration Planning and reasoning workflows Memory Workflow automation Ability to build agents that can interact with tools, APIs, databases and external systems. 6. LangChain / LangGraph Strong hands-on experience with LangChain and/or LangGraph .

Experience building LLM workflows and agent-based applications. Understanding of: Chains Agents Tools State management Graph-based workflows Agent orchestration Retrieval workflows Experience designing scalable Agentic AI workflows. 7. LLM Fine-Tuning Hands-on experience with LLM fine-tuning . Understanding of techniques such as: Supervised Fine-Tuning (SFT) Parameter-Efficient Fine-Tuning LoRA QLoRA Experience preparing datasets for fine-tuning.

Ability to evaluate fine-tuned models against baseline models. Understanding of model optimization and inference considerations. 8. BERT / LLaMA / Open-Source LLMs Experience working with one or more open-source / transformer-based models such as: BERT LLaMA / Llama Mistral Gemma Qwen Other open-source LLMs Candidate should understand model loading, inference, fine-tuning and evaluation. 9. PySpark Strong experience with PySpark for large-scale data processing.

Experience working with large datasets and distributed data processing. Knowledge of: Data transformations Data cleaning Aggregations Joins Spark SQL Performance optimization Ability to build scalable data processing pipelines. 10. Model Validation & Evaluation Experience validating and evaluating ML and GenAI models . Understanding of traditional ML evaluation metrics. Experience evaluating LLM/RAG applications using relevant quality metrics.

Ability to compare model performance and identify areas for improvement. Experience with: Accuracy Precision Recall F1 Score ROC-AUC Retrieval metrics LLM response quality Groundedness / relevance Experience designing evaluation datasets and test cases is preferred. 11. AI Tracing / Observability Experience with AI/LLM tracing and observability . Ability to monitor AI applications in production. Experience tracking: LLM requests/responses Latency Token usage Errors Retrieval performance Agent/tool execution Model performance Exposure to tools/frameworks such as LangSmith, OpenTelemetry, Arize Phoenix, MLflow or similar is preferred.

12. Model Deployment Experience deploying ML/LLM/GenAI solutions into production. Exposure to cloud and/or on-premise model deployment . Experience with model serving, APIs and production inference. Knowledge of deployment environments such as: AWS Azure GCP On-premise infrastructure Experience with Docker, APIs and CI/CD is an advantage. Key Responsibilities Design, develop and deploy Data Science, Machine Learning and GenAI solutions .

Build production-ready RAG and Agentic AI applications . Develop intelligent agents capable of tool calling, reasoning and multi-step task execution. Build LLM-powered applications using LangChain/LangGraph . Work with open-source LLMs including BERT, LLaMA and other transformer-based models . Fine-tune LLMs for specific business use cases. Develop scalable data processing pipelines using PySpark .

Perform data analysis, feature engineering and statistical modeling. Develop and maintain model validation and evaluation frameworks. Evaluate ML and LLM models using appropriate performance and quality metrics. Implement AI tracing, monitoring and observability for production GenAI systems. Deploy models and AI applications in cloud or on-premise environments. Optimize model performance, response quality, latency and cost.

Troubleshoot issues related to model inference, retrieval, agents and LLM workflows. Collaborate with Data Scientists, ML Engineers, Software Engineers and business stakeholders. Convert business requirements into scalable AI/ML solutions. Good to Have Experience with Vector Databases such as: FAISS Pinecone Weaviate Milvus Chroma Azure AI Search Experience with MLflow or similar ML lifecycle tools.

Experience with Docker/Kubernetes. Experience with REST APIs / FastAPI. Knowledge of cloud AI/ML services. Experience with MLOps / LLMOps. Experience with multi-agent frameworks other than LangChain/LangGraph. Experience working with enterprise GenAI applications. Ideal Candidate Profile The ideal candidate should be a Data Scientist / ML Engineer with strong GenAI and Agentic AI experience , rather than a pure Python developer.

A strong candidate would typically have: Data Science + Python + ML + Statistics + GenAI/LLM + RAG + Agentic AI + LangChain/LangGraph + LLM Fine-Tuning + Open-Source LLMs + PySpark + Model Evaluation + AI Observability + Model Deployment. Core Mandatory Skills Data Science, Python, Machine Learning, Statistics/ML Fundamentals, GenAI/LLM, RAG, Agentic AI, LangChain/LangGraph, LLM Fine-Tuning, BERT/LLaMA/Open-Source LLMs, PySpark, Model Validation/Evaluation, AI Tracing/Observability, Cloud/On-Prem Model Deployment.