AES - DE - Generative AI Prompt Engineers

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)

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)

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)