Job highlights

- Required Skills & Experience.
- Familiarity with: . GitHub / Azure DevOps repositories. CI / CD pipelines. Docker / Kubernetes (preferred)
- . 2+ years in GenAI / AI-led development (preferred)
- . 2+ years in GenAI / AI-led development (preferred) with in
- total experience

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)

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)

Positive 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)

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)

Disclaimer : This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 AES - DE - Generative AI Prompt Engineers (Bengaluru)
🏢 Zensar
📍 Bengaluru