DE&A - AIML - Deep Learning - Generative AI

Key Responsibilities

1. AI Agents for Application Development

Design and build AI agents that assist in application development lifecycle (SDLC)

Develop agents for:

Code generation & scaffolding

API development & integration

Code refactoring and optimization

Enable developer copilots for faster feature delivery

2. AI Agents for Application Enhancements

Build agents to:

Analyze existing codebases and suggest enhancements or optimizations

Automate bug detection and resolution

Support impact analysis for changes

Develop agents for legacy modernization and code migration (e.g., Java/.NET upgrades)

3. Testing & QA Automation Agents

Create agents to:

Automatically generate unit, integration, and regression test cases

Perform test execution and defect prediction

Enable self-healing test automation frameworks

4. LLM & Agent Framework Implementation

Build solutions using frameworks such as:

LangChain, LlamaIndex, Semantic Kernel, AutoGen, CrewAI

Implement:

Multi-agent orchestration (planner, executor, reviewer agents)

Tool-using agents (Git, CI/CD, APIs, databases)

5. RAG & Context Engineering

Implement RAG pipelines using application code repositories, documentation, and APIs

Build context-aware agents using:

Codebases (GitHub, Azure DevOps)

Knowledge repositories (Confluence, SharePoint)

6. DevOps & Integration

Integrate agents into:

CI/CD pipelines (Azure DevOps, GitHub Actions)

Developer tools (IDE plugins, Copilot extensions)

Develop APIs/microservices to expose agent capabilities

7. Evaluation & Optimization

Define metrics for:

Developer productivity improvement

Code quality and defect reduction

Optimize for cost, latency, and accuracy of LLM usage

8. Governance & Security

Ensure:

Secure code handling and IP protection

Compliance with enterprise AI governance

Guardrails to prevent insecure or non-compliant code generation

Required Skills & Experience

Core Skills

Strong programming skills in Python (mandatory) and at least one of Java/.NET/Node.js

Hands-on experience with application development & SDLC processes

Experience with REST APIs, microservices architecture

AI / GenAI Skills

Experience building AI-powered developer tools or agents

Strong knowledge of:

LLMs (OpenAI, Azure OpenAI, open-source models)

Prompt engineering & fine-tuning basics

Experience in RAG-based solutions

Agent Frameworks

Hands-on with:

LangChain / Semantic Kernel / LlamaIndex

Exposure to AutoGen / CrewAI / multi-agent patterns

DevOps & Tools

Familiarity with:

GitHub / Azure DevOps repositories

CI/CD pipelines

Docker / Kubernetes (preferred)

Good to Have

Experience with GitHub Copilot or similar developer productivity tools

Exposure to code analysis tools (SonarQube, SAST/DAST)

Experience in legacy modernization projects

BFSI domain experience (for enterprise use cases)

Experience

5–10 years total experience

2+ years in GenAI / AI-led development (preferred)