Please share CV to [Confidential Information] with the below details:
Total Experience- Should be between 12 to 20 yrs only
Current CTC-
Expected CTC-
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Short summary of your roles and responsibilities-
MANAGER- GENERATIVE AI
Please note: Only shortlisted candidates will be contacted.
Why This Role Exists
We are past the proof-of-concept phase. Dozens of teams across the organisation are already experimenting with generative AI — in code review, content generation, data pipelines, customer support, and more. The problem is that every team is reinventing the wheel, and very few can answer the simple question:
Is our AI investment actually working
This role is the connective tissue between teams. You will roll up your sleeves, learn what each team is doing, bring proven patterns to teams that haven't discovered them yet, build a measurement framework that makes AI impact undeniable, and define the engineering standards that let us scale safely.
Core Responsibilities
1. Hands-On AI Engineering & Experimentation
- Personally prototype and stress-test new tools, models, and frameworks before recommending them
- Contribute production-quality code to shared libraries and internal AI platforms
2. AI Product Development Lifecycle (AI PDLC) Leadership
- Define and own the organisation's AI PDLC: problem framing, data strategy, model selection, prompt engineering, safety review, launch criteria, and deprecation
- Embed AI PDLC gates into existing SDLC / agile ceremonies without creating bureaucracy
- Create lightweight templates and checklists teams can self-serve
- Review AI feature designs across teams and provide expert feedback
3. Cross-Team Discovery & Institutionalisation
- Maintain a living what's happening in AI map across all teams and business units
- Identify duplicated effort and broker shared solutions or platforms
- Run internal showcases and reverse-demo sessions so successful patterns spread
- Build a tiered adoption framework: Explore, Validate, Scale, Retire
- Partner with Platform/DevEx to bake AI tooling (GitHub Copilot, internal GPT gateways, vector DBs) into the standard developer environment
4. Metrics, Measurement & Proof of Value
- Design the company's GenAI metrics framework covering developer productivity, quality, cost efficiency, and business outcomes
- Work with analytics and data engineering teams to instrument AI systems and collect reliable signal
- Produce a recurring AI Impact Report — quantitative, honest, and leadership-ready
- Establish benchmarks and SLOs for model quality and system reliability
- Build the business case narrative that connects AI investment to revenue, cost savings, or risk reduction
5. Standards, Governance & Safety
- Author and maintain the internal GenAI Engineering Playbook (prompting, RAG, agents, evals, cost control, PII handling)
- Establish a model / vendor selection and deprecation process
- Own the AI risk register and coordinate with Legal, Security, and Compliance on review gates
6. Community & Enablement
- Run the AI Guild — fortnightly sessions mixing demos, deep-dives, and open Q&A
- Maintain a curated internal AI learning hub (papers, tutorials, vendor docs, internal case studies)
- Hire, mentor, or embed AI champions within product engineering teams
What We're Looking For
Must-Have Experience
- 12+ years in software engineering or machine learning,
- Designed or operated an AI/ML PDLC in a product company
- Built and shipped metrics frameworks that proved business value of AI initiatives
- Strong software engineering fundamentals: you write code, review PRs, and can debug a broken pipeline
- Excellent communicator who can translate between executive strategy and engineering specifics
Strong Differentiators
- AI CoE lead in a multi-team organisation
- Published internal playbooks, design systems, or engineering standards that were widely adopted
- Familiarity with evaluation frameworks: RAGAS, LLM-as-judge, human eval pipelines
- Experience with agentic systems (LangChain, AutoGen, CrewAI, custom tool-use architectures)
- Background in developer productivity measurement (DORA, SPACE, or custom frameworks)
- Prior experience influencing without authority across multiple product teams
Technology Context
You will work across a diverse tech stack. We don't expect you to be expert in everything, but you should be comfortable getting up to speed quickly.
LLM APIs
OpenAI (GPT-4o), Anthropic Claude, Azure OpenAI, Google Gemini
Orchestration
LangChain, LlamaIndex, custom tool-use agents
Vector Stores
Pinecone, pgvector, Weaviate
Infra
AWS / Azure, Kubernetes, MLflow, Weights & Biases
Languages
Python (primary), TypeScript, SQL
Dev Tooling
GitHub Copilot, CI/CD pipelines, Datadog / Grafana
