Job Summary

Job Description: Senior Data Scientist – Generative AI & Agentic AI

Experience: 6–10 Years

Relevant GenAI Experience: 3+ Years

Employment Type: Full-time

Role: Enterprise AI / Data Science Engineering

Role Overview

We are looking for an experienced Data Scientist / Generative AI Engineer with strong expertise in Machine Learning, Deep Learning, Large Language Models (LLMs), Generative AI, and Agentic AI.

The candidate should have hands-on experience designing and developing enterprise-grade AI applications, building scalable APIs, integrating multiple data sources and enterprise systems, and deploying production-ready AI solutions.

The ideal candidate must possess strong Python programming skills, API development experience, and the ability to translate complex business requirements into scalable AI-driven solutions.

Key Responsibilities

Key Responsibilities

1. Data Science & Machine Learning

- Design, develop, and implement machine learning and deep learning models.

- Work on supervised, unsupervised, and predictive modelling techniques.

- Perform data preprocessing, feature engineering, model evaluation, and optimization.

- Apply NLP, information retrieval, classification, clustering, and statistical techniques.

- Develop and optimize end-to-end data science pipelines.

2. Generative AI & LLM Engineering

- Design and develop enterprise-grade Generative AI applications using LLMs.

- Work with models such as GPT, Claude, Gemini, Llama, and other open-source models.

- Implement Retrieval-Augmented Generation (RAG) architectures.

- Develop advanced document processing, semantic search, embeddings, and vector search solutions.

- Implement prompt engineering, context management, token optimization, and response evaluation.

- Work with fine-tuning techniques such as LoRA, QLoRA, and PEFT where required.

3. Agentic AI & Multi-Agent Systems

- Design and implement autonomous AI agents and multi-agent architectures.

- Work with frameworks such as LangGraph, LangChain, AutoGen, CrewAI, and Semantic Kernel.

- Develop agent orchestration, planning, reasoning, tool calling, and memory management.

- Integrate external tools, APIs, databases, and enterprise applications with AI agents.

- Implement human-in-the-loop workflows, guardrails, and agent execution monitoring.

4. API Development & Backend Engineering

- Strong hands-on experience developing RESTful APIs using Python and FastAPI/Flask.

- Develop scalable backend services to expose AI/ML models through APIs.

- Implement API integrations with third-party applications and enterprise platforms.

- Experience with authentication, authorization, OAuth2, JWT, API keys, and secure API communication.

- Work with asynchronous processing, background tasks, webhooks, and microservices.

- Design API request/response schemas, error handling, logging, and versioning.

- Experience with API documentation using Swagger/OpenAPI.

5. Enterprise Data & Integration

- Work with structured, semi-structured, and unstructured data.

- Strong SQL knowledge and experience working with relational and NoSQL databases.

- Experience with vector databases such as Pinecone, Weaviate, Milvus, Qdrant, or FAISS.

- Integrate AI solutions with enterprise systems, databases, data warehouses, and document repositories.

- Build data ingestion, transformation, indexing, and retrieval pipelines.

- Experience with Kafka or other event-driven architectures is an advantage.

6. Cloud, Deployment & MLOps

- Experience deploying enterprise AI solutions on AWS, Azure, or GCP.

- Familiarity with services such as Azure OpenAI, AWS Bedrock, Vertex AI, and managed ML platforms.

- Containerization using Docker and orchestration using Kubernetes.

- Implement CI/CD pipelines and automated deployment workflows.

- Experience with MLflow, model monitoring, experiment tracking, and model lifecycle management.

- Optimize application performance, scalability, latency, and infrastructure costs.

7. Responsible AI, Evaluation & Security

- Implement LLM evaluation frameworks such as RAGAS, DeepEval, and LangSmith.

- Build evaluation pipelines for accuracy, relevance, faithfulness, and hallucination detection.

- Implement enterprise-grade security, PII masking, access control, and data privacy.

- Experience with prompt injection prevention, content filtering, and AI guardrails.

- Monitor production AI applications for quality, performance, and reliability.

Skill Requirements

Required Technical Skills

Skill Area| Required Technologies

Programming| Python, OOP, Data Structures

Data Science| NumPy, Pandas, Scikit-learn

Deep Learning| PyTorch, TensorFlow

GenAI / LLM| GPT, Claude, Gemini, Llama

RAG| LangChain, LlamaIndex, Embeddings

Agentic AI| LangGraph, AutoGen, CrewAI

API Development| FastAPI, Flask, REST APIs

Databases| SQL, PostgreSQL, MongoDB

Vector DB| FAISS, Pinecone, Qdrant, Milvus

Cloud| AWS, Azure, GCP

DevOps| Docker, Kubernetes, CI/CD

Evaluation| RAGAS, DeepEval, LangSmith

Version Control| Git, GitHub, GitLab

Mandatory Requirements

- Strong hands-on Python programming and software engineering skills.

- Practical experience building and deploying enterprise-grade GenAI applications.

- Strong understanding of LLM architecture, RAG, embeddings, and vector databases.

- Hands-on experience building REST APIs using FastAPI or Flask.

- Experience integrating AI models with external APIs and enterprise applications.

- Practical knowledge of SQL and database integrations.

- Understanding of cloud deployment, Docker, and production environments.

- Ability to independently develop end-to-end AI solutions.

Other Requirements

Good to Have

- Experience with Model Context Protocol (MCP).

- Knowledge of Graph RAG and Knowledge Graphs.

- Experience with Deep Research and autonomous research agents.

- Experience with multimodal AI (text, image, audio, video).

- Experience with real-time voice AI and conversational AI systems.

- Knowledge of Kubernetes, Terraform, and infrastructure automation.

- Experience working with enterprise clients and large-scale production systems.

Candidate Profile

We are looking for an engineer who is not limited to experimentation or notebook-based model development.

The candidate must be capable of taking an AI use case from requirement gathering and architecture design through development, API integration, deployment, monitoring, and production support.

The primary selection criteria will be hands-on coding capability, strong Python/API engineering, practical GenAI implementation, and enterprise solution development experience.