Home/Jobs/Sr Tech Lead-GenAI - VectorDBand PostgreSQL

Sr Tech Lead-GenAI - VectorDBand PostgreSQL

HCLTech

Chennai

5+ years

1 day ago

$27.7K–44.6K/yr

Full-time

Onsite

Skills Required

LLM

RAG

Agentic AI

Claude APIs

Vector Database

LangChain

Microsoft Copilot Studio

AWS Bedrock

AWS

Dataverse

GitHub

Git

Bitbucket

Docker

EKS

Description

Develop complex Agentic AI solutions in an Agile environment as part of a global team of engineers, architects, designers, and product experts.

Location: Chennai, Tamil Nadu

Experience

- 5+ years of professional software engineering or enterprise technology experience
- 2+ years of experience building AI-powered applications, intelligent automation, or conversational AI solutions
- Experience with Agentic AI platforms including AWS Agentcore, Bedrock, Microsoft Copilot Studio, Claude, or similar LLM-based platforms
- Experience with enterprise integration technologies including Microsoft Graph API, REST APIs, Dataverse, SharePoint, Teams, and SaaS platforms
- Experience designing and implementing Retrieval-Augmented Generation on postgres solutions and knowledge-grounded AI experiences
- Experience with prompt engineering, agent orchestration, AI workflows, function calling, and enterprise AI governance
- Experience with secure software development practices, Responsible AI principles, and cloud security standards
- Experience leveraging AI-assisted development tools such as GitHub Copilot, Claude code, or equivalent AI coding assistants
- Experience with cloud technologies including AWS, Docker, EKS, Lambda, SNS/SQS, and cloud-native architectures
- Experience with source control and development workflows including GitHub, BitBucket, and Git
- Experience with logging, monitoring, and observability tools such as AppDynamics, ArgoCD, CloudWatch, Splunk, and enterprise AI monitoring solutions
- Experience evaluating AI solution quality, automation effectiveness, and operational performance
- Familiarity with vector databases, semantic search, knowledge management platforms, and enterprise AI architectures
- Proven ability to convert ideas into Agentic solution and deliver results in a fast-paced Agile environment
- Proven track record of developing and supporting large-scale business-critical platforms and enterprise solutions
- Excellent analytical and problem-solving skills with the ability to evaluate AI outputs, identify risks, and recommend improvements
- Must be comfortable in a quick-paced, evolving technology landscape
- Strong presentation, communication,



and collaboration skills with the ability to create and maintain technical documentation and architectural artifacts

Qualification

- Bachelor’s degree in computer science, Engineering, Artificial Intelligence, or related field
- Microsoft AI-102, PL-400, PL-600, AWS AI Practitioner, or equivalent AI/Cloud certifications

Responsibilities

- Design, develop, and deploy AI agents and intelligent workflows
- Conduct and lead technical design discussions across multiple technology disciplines
- Propose viable AI and automation solutions to complex business problems
- Develop strong relationships across the business to influence strategic objectives
- Assist on-shore and off-shore teams with AI agent orchestration, prompt engineering, enterprise integrations, and cloud-native architecture
- Mentor and develop new or less experienced team members
- Liaise between software engineering teams and production support for operational readiness
- Provide support for escalated issues and AI solution defects
- Coordinate non-production deployments and AI solution validation activities with engineering and testing teams
- Partner with Release Management teams for production deployments of AI-powered capabilities
- Partner with SaaS platform providers and AI vendors to deliver program capabilities
- Coordinate enterprise changes and integrations impacting AI agents, knowledge sources, APIs, and enterprise systems
- Oversee AI governance, Responsible AI practices, security reviews, and compliance processes
- Analyze application logs and telemetry to identify root causes and optimize AI solution performance
- Build and maintain deployment, release, and governance processes for AI platforms and agentic solutions
- Design and implement RAG architectures and integrate enterprise knowledge sources
- Develop prompt engineering strategies, agent orchestration workflows, and human-in-the-loop business processes
- Evaluate and optimize AI agent performance, response quality, accuracy, and adoption metrics

Nice To Have

- Experience with Microsoft Copilot Studio, Claude, and AWS Bedrock
- Experience implementing multi-agent orchestration and agentic workflow patterns




- Experience with Python, ReactJS, LangChain, Semantic Kernel, MCP, or AI orchestration frameworks
- Insurance, Financial Services, Healthcare, or other highly regulated industry experience
- Experience establishing enterprise AI governance, AI Centers of Excellence, or Copilot adoption programs
- Experience building employee productivity intelligent assistants or enterprise knowledge agents

Other
- Agile environment
- Global team of engineers, architects, designers, and product experts

More Skills enterprise AI platforms, intelligent workflows, technical design, AI agent orchestration, prompt engineering, enterprise integrations, cloud-native architecture, production support, release management, SaaS platforms, AI vendors, enterprise changes and integrations, AI governance, Responsible AI, security reviews, compliance processes, Splunk, CloudWatch, AppDynamics, ArgoCD, Copilot analytics, AI monitoring tools, monitoring, observability, alerts, operational dashboards, deployment processes, release processes, governance processes, enterprise knowledge sources, human-in-the-loop, AI agent performance, response quality, accuracy, adoption metrics, AWS Agentcore, Bedrock, Microsoft Graph API, REST APIs, SharePoint, Teams, postgres, function calling, GitHub Copilot, Claude code, plugin, MCP, Lambda, SNS/SQS, semantic search, knowledge management platforms, Python, ReactJS, Semantic Kernel, MCP (Model Context Protocol), AI orchestration frameworks, Insurance, Financial Services, Healthcare, enterprise AI governance, AI Centers of Excellence, Copilot adoption programs, employee productivity intelligent assistants, enterprise knowledge agents, AI-102, PL-400, PL-600, AWS AI Practitioner

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