Coimbatore Tamil Nadu Validation engineer Jobs – 127 Open Jobs Today

Lead Java Engineer - AI Native
EPAM Systems, Inc. —Coimbatore, Tamil Nadu
- Azure
- English
- Java
- Health insurance
- Paid time off
Embedded Firmware Engineer
Karaka eDrives —Tvs Nagar, Coimbatore, Tamil Nadu
- ₹15,000 - ₹18,000 a month
- Full-time
- Computer science
- C++
- Bachelor's degree
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9h

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Robotics & Automation Engineer
QuantumPulse Technologies —Coimbatore, Tamil Nadu
In this role, you will design, prototype, and deploy intelligent robotic systems and automated hardware solutions. Job Type:* Full-Time (On-site).
₹5,50,000 - ₹9,00,000 a year
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Networking & IT Infrastructure Engineer
QuantumPulse Technologies —Coimbatore, Tamil Nadu
Working out of our innovation hub, you will collaborate closely with hardware, embedded software, and systems engineering teams. Job Type:* Full-Time (On-site).
₹4,50,000 - ₹7,50,000 a year
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Lead Java Engineer - AI Native

EPAM Systems, Inc. -
Coimbatore, Tamil Nadu

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Job details

Benefits

Health insurance
Paid time off

Qualifications

Jira
Spring Boot
CI/CD
Azure
Law
MCP
Enterprise Software
Git
English
Java
Master's degree
Microservices
AWS
Confluence
Distributed systems
Continuous integration
REST
GitHub
APIs
ServiceNow
AI
Spring framework
SDLC
Design patterns

Full job description

EPAM is a leading global provider of digital platform engineering and development services. We are committed to having a positive impact on our customers, our employees, and our communities. We embrace a dynamic and inclusive culture. Here you will collaborate with multi-national teams, contribute to a myriad of innovative projects that deliver the most creative and cutting-edge solutions, and have an opportunity to continuously learn and grow. No matter where you are located, you will join a dedicated, creative, and diverse community that will help you discover your fullest potential.

We are looking for a Lead Java Engineer – AI Native to design and scale enterprise Java systems while pioneering AI-native engineering practices across the SDLC. This role combines deep Java architecture expertise with hands-on experience building agentic pipelines and MCP server ecosystems that connect enterprise systems to LLM-based agents.

The role requires 3 days a week working from the office and involves mentoring engineering teams while driving AI adoption at scale.

Responsibilities

Design, develop and maintain scalable Java applications using Spring Boot and microservices architecture, owning features end-to-end with a high degree of autonomy
Build and deploy Model Context Protocol (MCP) servers that expose Java services, databases or internal tools to LLM-based agents, enabling agents to act on live enterprise data and systems
Architect end-to-end agentic SDLC pipelines including automated specification drafting, AI-driven code generation, intelligent test creation, CI/CD integration and deployment validation orchestrated by AI agents
Integrate agentic pipelines with enterprise tools and platforms such as Jira, Confluence, GitHub, ServiceNow and observability stacks via MCP connectors or REST/event-driven APIs
Apply AI coding assistants and frontier LLMs across the full development lifecycle daily and critically evaluate AI outputs for correctness, security and edge cases before committing
Bring an AI-first mindset to automate repetitive engineering tasks, measure outcomes rather than activity and identify AI-leverage opportunities within the delivery area
Contribute to the team's shared library of prompt templates, reusable agent patterns and MCP connectors
Conduct code and architecture reviews and mentor Junior and Mid-level engineers in Java best practices and AI-native engineering methods
Maintain strong automated test coverage across unit, integration, contract and AI-generated tests along with healthy CI/CD pipeline practices
Track frontier developments such as new model releases, emerging agent frameworks and new MCP connectors and bring relevant changes back to the team within weeks

Requirements

8–12 years of professional Java development experience with clear ownership of complex production systems
Expertise in Spring Boot, Spring Cloud and Spring Data along with Spring Security and microservices design patterns
Understanding of distributed systems, event-driven architecture and domain-driven design (DDD) plus CQRS/ES
Proficiency in cloud-native engineering on AWS, GCP or Azure including IaC, serverless patterns and managed services
Background in leading technical teams across architecture governance, coding standards and mentoring
Daily hands-on proficiency in AI coding assistants such as GitHub Copilot, Cursor and Claude Code and frontier LLMs including Claude, GPT-4o and Gemini, with capability to coach a team of 8-15 engineers in AI-native practices
Hands-on expertise in designing, building and deploying MCP server ecosystems at project or account scale including security controls, versioning and observability
Capability to architect and operate end-to-end agentic SDLC pipelines integrated with enterprise tools via MCP and APIs in production environments
Skills in evaluating and selecting AI agent orchestration frameworks such as LangGraph, CrewAI and AutoGen or Spring AI Agents for production use with documented rationale and trade-offs
Showcase of improving a team's AI maturity supported by adoption metrics or productivity evidence
Demonstrated learning agility at team scale with evidence of driving meaningful changes to engineering practices in the last 12 months due to evolving frontier models and tools
English proficiency at Upper-Intermediate level or above (B2+)

Nice to have

Experience with RAG pipelines, LLM fine-tuning or LLM evaluation frameworks such as RAGAS and DeepEval applied to software engineering contexts
Familiarity with structured agentic SDLC methodologies including specification-driven AI development and specification hardening or equivalent governed delivery protocols
Experience with Managed Services or AIOps delivery models such as autonomous monitoring, AI-assisted incident response and intelligent operations pipelines
Skills in function calling and tool-use design across multiple frontier models to build reliable governed tool-use chains
Contributions to internal AI maturity assessments, team certification programmes or AI engineering playbooks

We offer

Opportunity to work on technical challenges that may impact across geographies
Vast opportunities for self-development: online university, knowledge sharing opportunities globally, learning opportunities through external certifications
Opportunity to share your ideas on international platforms
Sponsored Tech Talks & Hackathons
Unlimited access to LinkedIn learning solutions
Possibility to relocate to any EPAM office for short and long-term projects
Focused individual development
Benefit package:
- Health benefits
- Retirement benefits
- Paid time off
- Flexible benefits
Forums to explore beyond work passion (CSR, photography, painting, sports, etc.)

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