Senior Java Engineer / Rating Scorecards / Complex Scorecards, VP

Position Overview

Job Title: AI Engineer - Agentic SDLC Expert (VP)

Corporate Title: Vice President

Location: Pune, India

Role Description

• We are seeking an experienced AI Engineer - Agentic SDLC Expert to lead the transformation of software engineering through the adoption of Generative AI, Large Language Models (LLMs), AI Agents, and AI-native software delivery practices.

  This role focuses on designing, building, and operationalising enterprise-scale Agentic SDLC platforms that automate and accelerate activities across the software development lifecycle, including requirements analysis, architecture design, implementation, testing, modernization, deployment, and continuous improvement.

  The successful candidate will combine deep software engineering expertise with modern AI engineering capabilities to create intelligent development workflows, AI-powered developer tools, and autonomous engineering agents that improve engineering productivity, software quality, and delivery speed across the organization.

  Unlike traditional software engineering roles, this position is centered on enabling and orchestrating AI-assisted software development at scale.

What we'll offer you

As part of our flexible scheme, here are just some of the benefits that you'll enjoy,

• Best in class leave policy.
• Gender neutral parental leaves
• 100% reimbursement under childcare assistance benefit (gender neutral)
• Sponsorship for Industry relevant certifications and education
• Employee Assistance Program for you and your family members
• Comprehensive Hospitalization Insurance for you and your dependents
• Accident and Term life Insurance
• Complementary Health screening for 35 yrs. and above

Your key responsibilities

Agentic SDLC Platform Engineering

• Design and implement Agentic SDLC platforms and AI-native engineering workflows.
• Build reusable AI-powered software delivery capabilities that support:
  • Requirements analysis
  • Impact analysis
  • Solution design
  • Architecture generation
  • Task decomposition
  • Code generation
  • Test generation
  • Documentation generation
  • Deployment readiness validation
  • Release support
• Develop engineering accelerators that improve productivity and engineering quality across multiple teams.

AI Orchestration Engineering

• Design and implement intelligent multi-agent engineering workflows.
• Configure and orchestrate:
  • Large Language Models (LLMs)
  • AI Agents
  • Agent Networks
  • Prompt Chains
  • Tool Calling Frameworks
  • Knowledge Retrieval Systems
  • Model Context Protocol (MCP) integrations
• Build autonomous and semi-autonomous workflows capable of coordinating multiple engineering activities.
• Establish governance and validation mechanisms for AI-generated outputs.

Developer Productivity Engineering

• Improve software delivery velocity through AI-assisted development workflows.
• Build capabilities that enable engineers to:
  • Generate code faster
  • Modernize legacy applications
  • Troubleshoot defects more efficiently
  • Increase test automation coverage
  • Accelerate onboarding and knowledge discovery
  • Reduce manual effort throughout the SDLC
• Measure and optimize developer productivity through engineering metrics and workflow analytics.

Engineering Platform Integration

• Integrate AI capabilities across enterprise engineering ecosystems, including:
  • GitHub Enterprise
  • GitHub Copilot
  • GitHub Actions
  • Source code repositories
  • CI/CD platforms
  • Knowledge repositories
  • Development portals and engineering platforms
• Create seamless workflows connecting engineering systems, AI platforms, and organizational knowledge assets.

AI Engineering Governance

• Define standards and best practices for AI-assisted software engineering.
• Implement:
  • Human-in-the-loop approval workflows
  • AI output validation mechanisms
  • Prompt governance standards
  • Model governance controls
  • AI auditability and traceability frameworks
• Ensure responsible, secure, and compliant adoption of AI technologies across engineering teams.

Engineering Transformation

• Lead adoption of AI-native engineering practices across development teams.
• Evaluate emerging AI technologies, frameworks, and development platforms.
• Identify opportunities to automate and optimize software delivery processes.
• Support engineering teams in transitioning towards AI-assisted and agent-based development methodologies.
• Drive innovation in software delivery through continuous experimentation and learning.

Your skills and experience

AI Engineering

Strong experience with:

• Large Language Models (LLMs)
• Generative AI applications
• AI Agent frameworks
• Agent orchestration
• Prompt engineering
• AI-assisted software development
• AI workflow automation

Agentic SDLC

Hands-on experience designing or implementing:

• AI-driven development workflows
• Autonomous engineering agents
• AI-enabled SDLC automation
• Engineering orchestration platforms
• Developer productivity platforms
• Software engineering automation pipelines

Modern AI Concepts

Strong understanding of:

• Retrieval-Augmented Generation (RAG)
• Model Context Protocol (MCP)
• Tool Calling
• Function Calling
• Agent-to-Agent Communication
• Multi-Agent Systems
• Grounded AI Systems
• Knowledge-Aware AI Applications
• AI Evaluation Frameworks

Software Engineering

Strong background in:

• Java Application Development
• Spring Boot
• API Design and Integration
• Enterprise Application Architecture
• Distributed Systems
• Build and Release Engineering
• Software Debugging and Troubleshooting

Understanding of:

• Modern Software Architecture
• CI/CD Practices
• DevSecOps
• Quality Engineering
• Testing Frameworks

Cloud & Platform Engineering

Experience with:

• GitHub Enterprise
• GitHub Copilot
• GitHub Actions
• Jenkins
• Maven
• OpenShift
• Kubernetes
• Docker
• GCP or Public Cloud Platforms

Desired Skills

Experience with:

• LangGraph
• OpenAI Agents SDK
• Graph-based Knowledge Systems
• Knowledge Graphs and Ontologies
• AI Governance Platforms
• Prompt Lifecycle Management
• Agent Evaluation Frameworks
• AI Observability
• Platform Engineering
• Internal Developer Platforms (IDP)
• Application Modernization
• Software Architecture

Engineering Mindset

• AI-first development
• Automation-driven
• Scalable and reusable design
• Continuous improvement

How we'll support you

• Training and development to help you excel in your career.
• Coaching and support from experts in your team.
• A culture of continuous learning to aid progression.
• A range of flexible benefits that you can tailor to suit your needs.

About us and our teams

Please visit our company website for further information:

We strive for a in which we are empowered to excel together every day. This includes acting responsibly, thinking commercially, taking initiative and working collaboratively.
Together we share and celebrate the successes of our people. Together we are Deutsche Bank Group.
We welcome applications from all people and promote a positive, fair and inclusive work environment.