Senior Engineer

Western Digital; Bengaluru; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Western Digital seeks an experienced AI Engineer to architect next-generation AI solutions, including agentic platforms, LLM-powered apps, and scalable production infrastructure.

You will work across client engagements, shape solutions, and provide technical oversight, staying close to code and guiding engineers and data scientists.

Qualifications

• 6-8 years of software engineering experience with Agentic AI and ML platforms in production.
• Formal degree in CS/IT/electronic/AI domains or equivalent.
• Strong foundation in data science and ML concepts.
• Hands-on with LLMs, prompting, and RAG.
• Understanding of how AI agents are built and orchestrated.
• Proficiency in Python and ML libraries; solid engineering habits.
• Clear communicator; collaborative team player.

Responsibilities

• Design, build, and deploy agentic AI systems using LLMs and multi-agent workflows.
• Debug production AI issues and apply fixes to maintain reliability.
• Evaluate agents to ensure robust production systems.
• Develop and optimize MLOps pipelines, RAG, and vector DB strategies.
• Build robust prompt frameworks, evaluation pipelines, and guardrails.
• Integrate LLM-based systems with enterprise data and APIs.
• Implement observability, feedback loops, and monitoring for production.

Skills

Design and build agentic AI systems

LLMs and tool orchestration

Multi-agent workflows

MLOps pipelines

Observability and monitoring

Python and ML libraries

Strong communication

Education

BE/B.Tech or ME/M.Tech in CS/IT/EC/DS/ML/AI

Tools

Vector databases

RAG (Retrieval Augmented Generation)

LLMOps

Job description

Job Summary

As our AI practice scales, we are seeking an experienced AI Engineer to act as the senior technical authority shaping how we design and deliver next‑generation AI solutions. This is a role for someone who has moved beyond building individual models to architecting entire systems: agentic platforms, LLM-powered applications, and the production infrastructure that makes them dependable at enterprise scale.

You will sit at the intersection of strategy and delivery, operating across multiple client engagements simultaneously. In the early phases you will shape solutions, validate technical approaches, and translate ambiguous business problems into robust, deployable architectures. As engagements mature, you will provide the technical oversight that keeps delivery teams aligned to the original design intent. Your influence will be felt in pre‑sales conversations, solution blueprints, and the technical standards that define how our practice builds.

This is a hands on architectural role, not a purely advisory one. We expect you to remain close to the code, to prototype where it accelerates a decision, and to set a credible technical example for the engineers and data scientists you guide.

Responsibilities

• Strong hands‑on engineer who can Design, build, and deploy agentic AI systems using LLMs, tool orchestration, and multi-agent workflows. Experience building multi‑agent systems or autonomous workflows.
• Proven expertise to structurally debug the AI production issue and apply the right fix.
• Experience evaluating the Agents so that the production system remain robust.
• Develop and optimize MLOps pipelines, RAG, vector databases and retrieval strategies.
• Build robust prompt frameworks, evaluation pipelines, and guardrails for safe and reliable AI behaviour.
• Integrate LLM‑based systems with enterprise APIs, data platforms, and operational systems.
• Implement observability, feedback loops, and performance monitoring for agentic systems in production.

Qualifications

To qualify for this role, you must have:

• 6-8 years of professional experience in software engineering with Agentic AI, ML platform engineering with 5+ years of hands‑on experience with Generative AI and LLM‑based systems in production environments.
• BE/B.Tech/ME/M.Tech Computer Science/IT/Electronics Engg/ Data Science/ Machine learning/AI Engineering or related field.
• Solid grounding in core data science and machine learning.
• Practical experience with LLMs and generative AI, including prompting and RAG.
• Familiarity with how AI agents are built and orchestrated.
• Strong Python and common data/ML libraries; good engineering habits.
• Clear communicator and collaborative team player.

Ideally, you ll also have, but not essential:

• Exposure to cloud AI platforms, Azure a plus.
• Experience practicing MLOps and LLMOps.
• Early experience mentoring junior colleagues.
• Interest in consulting and client‑facing delivery.