Data Scientist - Agentic AI

Job Description: AI Engineer

Location: Gurgaon

Years of experience: 3-7 years

Employment Type: Full-time

About Us

We turn customer challenges into growth opportunities.

Role Summary

We are looking for a highly skilled AI Engineer to build, optimize, and deploy production-grade AI solutions. In this role, you will be the engine room of our AI initiatives - taking architectural blueprints and turning them into scalable, functional systems. While you will partner with Data Engineering and Platform teams, your focus is on the implementation of LLM-based applications (RAG), agentic workflows, and fine-tuning models to meet the specific needs of our healthcare and pharmaceutical clients.

What You’ll Do

AI Development & Implementation

Build & Optimize: Develop and maintain LLM-powered applications using frameworks like Microsoft Agent Framework, AutoGen, LangChain, LangGraph, LlamaIndex etc..

RAG Pipelines: Implement and fine-tune Retrieval-Augmented Generation (RAG) pipelines, including sophisticated chunking strategies, embedding selection, and vector database management.

Agentic Systems: Develop autonomous agents capable of tool-use, multi-step planning, and human-in-the-loop interactions.

Model Fine-tuning: Execute fine-tuning and optimization tasks (Quantization, PEFT/LoRA) to adapt models for specific domain tasks.

MLOps & Productionization

Deployment: Deploy models into production environments using Docker and Kubernetes, ensuring high availability and low latency.

Monitoring: Implement observability for AI systems, tracking accuracy, hallucinations, cost, and latency.

CI/CD: Maintain CI/CD pipelines for ML, ensuring automated testing (unit, contract, and model-quality tests) is integrated into the workflow.

Domain-Specific Execution (Pharma/Healthcare)

Data Integrity: Work with sensitive pharmaceutical datasets, ensuring all AI outputs comply with data privacy standards and PII masking requirements.

Validation: Support the rigorous validation processes required in life sciences, including reproducibility and statistical validity of model outputs.

Collaboration & Mentorship

Teamwork: Work closely with Senior Architects and Engagement Managers to translate business requirements into technical tasks.

Code Quality: Participate in code reviews and contribute to the team’s internal library of reusable AI patterns and playbooks.

Must-Have Qualifications

Experience: 3-7 years of experience in Software Engineering or Data Science, with at least 2 years focused specifically on deploying AI/ML models in production.

Tech Stack: Expert-level Python skills and proficiency with deep learning frameworks (PyTorch or TensorFlow).

Generative AI: Hands-on experience building applications with LLMs (OpenAI, Claude, Gemini, etc.) and vector databases.

Cloud & DevOps: Strong experience with AWS/Azure/GCP and containerization (Docker/Kubernetes).

Engineering Fundamentals: Strong SQL skills, API design (FastAPI/Flask), and a "software engineering first" approach to ML (testing, modularity, and documentation).

Preferred Qualifications

Pharma Experience: Prior experience working with healthcare data, clinical trials, or life sciences digital transformation.

Advanced RAG: Experience with advanced retrieval techniques (reranking, hybrid search, query expansion).

Evaluation Frameworks: Experience using G-Eval, RAGAS, or TruLens to quantify LLM performance.

Performance Tuning: Knowledge of vLLM, DeepSpeed, or Triton Inference Server for high-throughput serving.