Data Scientist (LLM & Agentic AI)

We are looking for a highly motivated Data Scientist with hands-on experience in production-grade AI/ML systems, Large Language Models (LLMs), and Agentic AI applications. The ideal candidate should have experience building, evaluating, deploying, and monitoring AI solutions at scale, with strong expertise in cloud-native deployments on AWS (preferred).

You will work closely with Data Science, Engineering, Product, and Business teams to design and deploy intelligent AI solutions that deliver measurable business impact.

About the Role:

In this opportunity as a Data Scientist, you will:

• Design, develop, and deploy machine learning and generative AI solutions into production environments.

• Build and optimize applications powered by LLMs, including RAG, prompt engineering, tool calling, function calling, and agentic workflows.

• Develop autonomous and semi-autonomous AI agents using modern agentic frameworks.

• Fine-tune, evaluate, and benchmark open-source and commercial language models.

• Build scalable data pipelines and AI services on cloud platforms, primarily AWS.

• Work with vector databases and retrieval systems to improve knowledge-grounded AI applications.

• Collaborate with software engineers to productionize ML and GenAI solutions using MLOps best practices.

• Implement monitoring, observability, performance tracking, guardrails, and cost optimization for deployed AI systems.

• Conduct experiments and analyze model performance to drive continuous improvement.

• Stay current with advancements in LLMs, Agentic AI, MLOps, and cloud-native AI infrastructure.

• Work Mode: Hybrid-3 days from office.

• Shift Timing: 12pm IST to 9pm IST.

About You:   

You're a fit for the role of Data Scientist, if your background includes:

• Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Statistics, or related field.

• 2–4 years of experience developing and deploying machine learning solutions in production environments.

• Strong proficiency in Python and ML/AI libraries.

• Hands-on experience with Large Language Models (OpenAI, Anthropic, Llama, Mistral, etc.) and Generative AI applications.

• Experience building RAG-based systems using vector databases such as Pinecone, Weaviate, Chroma, FAISS, or OpenSearch.

• Knowledge of prompt engineering, model evaluation, hallucination mitigation, and agent orchestration techniques.

• Experience with cloud platforms, particularly AWS (SageMaker, Lambda, ECS/EKS, Bedrock, S3, API Gateway, etc.).

• Experience deploying AI/ML services using Docker and modern CI/CD practices.

• Good understanding of software engineering principles, APIs, and scalable system design.

• Strong problem-solving and analytical skills.

• Experience with agentic frameworks such as LangGraph, LangChain, CrewAI, or similar frameworks.

• Familiarity with MLOps tools and practices including MLflow, Airflow, monitoring platforms, and automated deployment pipelines.

• Experience with model serving, inference optimization, caching, and cost optimization techniques.

• Understanding of distributed systems, microservices, and event-driven architectures.

• Exposure to AWS Bedrock, knowledge graph integration, multi-agent systems, or advanced retrieval techniques.

• Contributions to open-source AI projects, technical blogs, research publications, or hackathons.