• Sit with functions — Products, Programs, AI/ML, Engineering, Design, Communications, HR, Finance, Admin, Partnerships — to learn how work actually gets done and where the real friction is.
  
• Distinguish problems worth solving from noise, and name them precisely before reaching for any tool.
  

• Prototype solutions hands-on — prompts and workflows, lightweight automations, agentic tooling, or integrations across the tools we run (Gemini for Workspace, Cursor, Claude Code, Canva, Figma) and others as needed.
  
• Evaluate honestly: measure time saved and quality against licence, token, learning, and maintenance cost — and be willing to conclude “not worth it.”
  
• Choose the right tool, including no AI at all. Recommend non-AI or non-agentic fixes — a template, a script, a checklist, a process change — whenever they beat the AI option on merit.
  

• Stand up and coach a cross-functional network of part-time AI champions (one or two per function); be their force multiplier and escalation point.
  
• Own the shared playbook: validated use cases, reusable prompts, and honest post-mortems that compound across functions.
  
• Build durable AI fluency across the org — capability over dependency — through hands-on enablement, not slideware.
  

• Maintain the approved-tools list and the data-classification rules; run the quick security/privacy gate before sensitive or partner data touches any tool.
  
• Vet vendors’ data-retention and model-training terms; prefer tools that do not train on our inputs.
  
• Own the cost picture — licences, tokens, and maintenance — and keep spend tied to demonstrated value.
  

Requirements

• A builder. Strong engineering background — you can read a codebase, write production-quality code, and ship working prototypes yourself. (Several years of hands-on software or ML engineering.)
  
• Fluent in modern AI. Practical, current command of LLMs, prompting, retrieval, agents, and AI-assisted dev tools — and a realistic sense of what they can and cannot do.
  
• Security- and privacy-literate. You understand data-handling risk, vendor data-retention and training terms, and how to keep sensitive data safe — instinctively, not as an afterthought.
  
• A clear-eyed economist of effort. You reason about cost versus benefit and total cost of ownership, and you are comfortable saying “the simpler, non-AI option wins here.”
  
• A translator and teacher. You can sit with a finance analyst or an HR partner, understand their world, and build with empathy. Excellent, simple, written communication — a Wadhwani non-negotiable.
  
• Self-starting and persistent. You define the problem, drive the solution, and see it through — without excuses, and with humility about what you don’t yet know.
  

• Prior Forward-Deployed, solutions-engineering, or internal-tools/platform experience.
  
• Exposure to the social / development sector, public health, agriculture, or government data environments.
  
• Experience standing up internal enablement, communities of practice, or developer-experience programmes.
  

Benefits

• Insurance Benefits - Medical Insurance, OPD Coverage, Accidental Insurance, Life Insurance.
  
• Wellness Program - Employee Assistance Program, Annual Health Check-Ups, Onsite Medical Centers, Emergency Support System.
  
• Parental Support - Maternity Benefit, Travel Reimbursement for Expectant Mothers during Last Trimester, Paternity Benefit, Day Care Support Program
  
• Mobility Benefits - Travel Policy, Relocation and Deputation benefits,
  
• Statutory Benefits - Employee PF Contribution, Gratuity, Leave Encashment
  
• Other Benefits – Unlimited Leave Policy, Professional Development Policy, Referral Bonuses, Internet and Telephone Allowance.