AI Enabled Engineering Leader
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Job summary
Qualifications
- Strong experience with AI frameworks and technologies.
- Ability to lead and influence multiple teams effectively.
- Experience with SDLC and software quality engineering.
Responsibilities
- Define and scale AI-enabled engineering practices.
- Act as a trusted advisor to Scrum teams.
- Lead the AI Engineering Community of Practice.
Skills
Education
Tools
Job description
Experience Range 12.0 - 16.0 Years
Qualification Graduate
Open
Aeries Technology is a Nasdaq listed global professional services and consulting partner, headquartered in Mumbai, India, with centers in the USA, Mexico, Singapore, and Dubai. We provide mid-size technology companies with the right mix of deep vertical specialty, functional expertise, and the right systems & solutions to scale, optimize and transform their business operations with unique customized engagement models. Aeries is Great Place to Work certified by GPTW India, reflecting our commitment to fostering a positive and inclusive workplace culture for our employees. Read about us at https://aeriestechnology.com
EQ Sirius US programme (Vega) scope is defined as the various outputs (Products) that will be created by the Work Streams that will enable the clients to be migrated from the FIS Sunstar system to the EQ Sirius systems. To deliver a transitional architecture that supports current (Sunstar) and new (Sirius) EQ solution platforms during the migration transition.
AI ‑ Enabled Engineering Leader (Delivery & Developer Experience)
Head of Engineering
This role is a senior, hands ‑ on engineering leadership position reporting directly to the Head of Engineering. The leader will define, implement, and scale AI ‑ enabled engineering practices that materially improve developer experience, delivery speed, and software quality across Scrum teams.
The role combines technical leadership, strategy, execution, and influence. You will work directly with Scrum teams to adopt AI across the SDLC—requirements, design, coding, testing, code review, non‑functional requirements, and CI/CD—while establishing best practices, guardrails, and a sustainable Community of Practice (CoP).
This is not an advisory role. You will build, pilot, coach, and scale.
1. Strategic Partner to the Head of Engineering
- Act as a trusted engineering leader and advisor to the Head of Engineering on AI adoption, developer experience, and delivery effectiveness
- Shape the engineering strategy for AI ‑ enabled software delivery
- Translate strategy into executable plans adopted by Scrum teams
2. Hands ‑ On Enablement with Scrum Teams
- Work directly with Scrum teams to embed AI into day ‑ to ‑ day delivery
- Pair with engineers on real work:
- requirements clarification and acceptance criteria
- design and technical discovery
- code generation and refactoring
- unit, integration, and functional test creation
- pull request reviews and release readiness
- Identify friction points and continuously improve practices and tooling
- Define and operationalize AI usage across the full SDLC:
- Development & refactoring
- Testing (unit, functional, integration)
- Code review and quality gates
- CI/CD and release automation
- Create clear, practical “ how we build software here ” standards
4. Best Practices, Standards & Guardrails
- Establish best practices for responsible AI usage :
- validation and review of AI ‑ generated code
- test and security expectations
- documentation and traceability
- Define lightweight standards that enable speed rather than constrain it
- Produce templates, examples, prompt patterns, and checklists teams actually use
5. Developer Experience & Tooling
- Integrate AI tools seamlessly into the developer workflow:
- code reviews and PR automation
- testing frameworks
- Improve the developer “inner loop”:
- faster feedback
- reduced manual toil
- Build reference implementations and POCs for agent-based GenAI systems
- Support engineering teams moving from experimentation to production
- Create reusable templates, libraries, and example repositories
6. Community of Practice (CoP) Leadership
- Create and lead an AI Engineering Community of Practice
- Build a sustainable model including:
- playbooks and shared libraries
- demos and office hours
- engineering champions across teams
- continuous feedback and iteration
- Ensure practices evolve as tools and needs change
- Evangelize practical usage of GenAI and agentic AI systems across engineering teams
- Act as an enabler and trusted technical advisor to engineering teams
- Educate engineers on LLM-powered agents, tool-using agents, and human-in-the-loop workflows
- Run workshops, demos, brown-bag sessions, and internal documentation
- Help teams adopt AI safely and pragmatically without disrupting delivery
7. Measurement & Continuous Improvement
- Define success metrics aligned with engineering and business outcomes:
- cycle time and lead time
- deployment frequency
- CI/CD health
- Run pilots, measure results, and scale what works
- Strong hands ‑ on software engineering background
- Experience leading engineering initiatives across multiple teams
- Deep understanding of SDLC, CI/CD, and quality engineering practices
- Proven ability to drive adoption through influence and coaching
- Excellent communication and presentation skills
- Comfortable operating as a senior leader reporting directly to the Head of Engineering
- Experience improving developer experience or engineering productivity at scale
- Experience modernizing test automation and CI/CD pipelines
- Experience creating and sustaining Communities of Practice
- Exposure to security, reliability, and observability standards in production systems
- AI Frameworks - Hands ‑ on experience with PyTorch, TensorFlow, Hugging Face Transformers, LangChain, LlamaIndex, ONNX Runtime, and vector database tooling.
- Agentic & GenAI Skills - Expertise in building AI agents, prompt engineering, RAG pipelines, workflow orchestration (AutoGen, LangGraph), and integrating LLMs across the SDLC.
- System Architecture - Strong grounding in distributed systems, event ‑ driven design, microservices, API architecture, observability patterns, and scalable cloud ‑ native design.
- Testing & Quality Engineering - Deep experience with automated unit/functional/integration testing, contract testing, mutation testing, test data generation, and AI ‑ assisted test engineering.
- AI Ops / MLOps - Working knowledge of model deployment, monitoring, drift detection, evaluation, governance, prompt lifecycle management, and AI risk/guardrail frameworks.
- Cloud & DevOps - Hands ‑ on expertise with Azure/AWS/GCP, CI/CD (GitHub Actions, GitLab, Azure DevOps), containerization (Docker, Kubernetes), and cloud ‑ native AI services.
- AI ‑ enabled engineering practices adopted by most Scrum teams
- Measurable improvements in delivery speed, quality, and predictability
- Reduced friction in development and CI/CD workflows
- A thriving Community of Practice that sustains adoption
- Clear executive visibility into engineering improvements and outcomes
This role ensures that AI adoption in Engineering is practical, responsible, and impactful. Reporting directly to the Head of Engineering, this leader shapes how software is built—improving outcomes for developers, the business, and customers.
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