Data Scientist with AI ML

HCLTech; Bengaluru; On-site; INR 4,500,000 - 7,000,000; Full time

Job summary

HCL Technologies Limited is seeking a Senior Full Stack Data Scientist with deep ML and software engineering expertise to own end-to-end AI product lifecycles. You will build and deploy GenAI, RAG and Graph RAG solutions, design agent workflows and integrate them with enterprise systems.

Join a team driving production-grade AI on AWS, containerized services, observability, and scalable data pipelines, while ensuring impact through robust testing and architecture reviews.

Qualifications

• 8+ years of experience across data science, ML, AI engineering or software engineering.
• Strong Python coding, OOP, APIs, testing and system design skills.
• Experience deploying ML/GenAI solutions in production.
• Hands-on knowledge of RAG, embeddings, vector databases, ingestion and retrieval design.
• At least one enterprise Agentic AI implementation with architectural or development contribution.
• Experience with AWS, containerization, CI/CD and production observability.
• Strong analytical thinking and stakeholder communication.

Responsibilities

• Define hypothesis-driven approaches for ambiguous business and technical problems.
• Perform exploratory data analysis, feature engineering, model selection, validation and error analysis.
• Build and evaluate supervised, unsupervised and generative AI solutions using appropriate metrics.
• Create reproducible experimentation, testing and model-evaluation practices.
• Design production-grade LLM applications using prompt engineering, embeddings and retrieval pipelines.
• Own document ingestion, parsing, chunking, indexing, retrieval and grounded response generation.
• Design Graph RAG solutions combining knowledge graphs with semantic/vector retrieval.
• Define evaluation measures for retrieval quality, faithfulness and latency.
• Design agent workflows with planning, reasoning, tool use and multi-step execution.
• Integrate agents with enterprise APIs and data platforms; ensure observability and reliability.
• Debug, optimize agent behavior, handle failures, retries and routing.
• Develop and operate AI services on AWS; containerize and deploy via CI/CD pipelines.
• Implement logging, metrics, tracing, alerting and dashboards for applications and pipelines.

Skills

Python

APIs

OOP

Testing

System design

Data science

ML engineering

RAG

Embeddings

Vector databases

Retrieval design

Agentic AI

AWS

CI/CD

Containers

REST APIs

GenAI

Education

Tools

Docker

AWS

SageMaker

CI/CD

REST APIs

Job description

We are seeking a Full Stack Data Scientist with strong software-engineering fundamentals and hands-on expertise in machine learning, Generative AI, Retrieval-Augmented Generation (RAG), Graph RAG and Agentic AI. The role owns the complete solution lifecycle, from problem framing and data ingestion through model and agent development, cloud deployment, observability and production support.

Experience Range - 11 to 14 Years

Data Science & Machine Learning

• Define hypothesis-driven approaches for ambiguous business and technical problems.
• Perform exploratory data analysis, feature engineering, model selection, validation and error analysis.
• Build and evaluate supervised, unsupervised and generative AI solutions using appropriate business and technical measures.
• Create reproducible experimentation, testing and model-evaluation practices.

Generative AI, RAG & Graph RAG

• Design production-grade LLM applications using prompt engineering, embeddings, vector databases and retrieval pipelines.
• Own document ingestion, parsing, chunking, metadata enrichment, indexing, retrieval, reranking and grounded response generation.
• Design Graph RAG solutions combining knowledge graphs with semantic or vector retrieval where relationship-aware reasoning is required.
• Define evaluation measures for retrieval quality, answer relevance, faithfulness, hallucination control, latency and cost.

Agentic AI & Workflow Automation

• Design single-agent and multi-agent workflows supporting planning, reasoning, tool use and multi-step execution.
• Implement context management, short-term and durable memory, function or tool calling, guardrails and human-in-the-loop controls.
• Integrate agents with enterprise APIs, data platforms and business workflows.
• Debug and optimize agent behavior, failure handling, retries, routing and task completion.

Full Stack & Legacy Modernization

• Build Python services, REST APIs and microservices using sound object-oriented design and clean-code practices.
• Integrate AI services with web applications and enterprise systems; frontend exposure to React or Angular is desirable.
• Assess legacy code and data flows, identify modernization opportunities, and apply incremental refactoring and migration best practices.
• Participate in architecture reviews, code reviews, testing and secure engineering practices.
• Design and operate AI services on AWS using suitable services such as S3, Lambda, ECS, EKS, Bedrock, SageMaker, IAM, VPC, RDS or DynamoDB.
• Containerize workloads using Docker and deploy through repeatable CI/CD pipelines.
• Apply MLOps practices covering experiment tracking, model and prompt versioning, testing, release controls and rollback.
• Implement logging, metrics, tracing, alerting and dashboards for applications, retrieval pipelines, models and agents.
• Use observability to investigate latency, retrieval quality, tool failures, token consumption, reliability and production incidents.
• Make engineering trade-offs across scalability, security, performance, resilience, maintainability and cost.

Skill Requirements

Mandatory Experience

• 8+ years of relevant experience across data science, machine learning, AI engineering or software engineering.
• Strong hands-on Python coding experience and practical knowledge of OOP, APIs, testing and system design.
• Experience building and deploying ML or GenAI solutions in production.
• Hands-on knowledge of RAG, embeddings, vector databases, ingestion and retrieval design.
• At least one enterprise Agentic AI implementation with direct contribution to architecture or development.
• Experience with AWS, containerization, CI/CD and production observability.
• Strong analytical thinking, problem solving and stakeholder communication.