Artificial Intelligence Engineer

Key Responsibilities

Embed: one function at a time; shadow the work; map the real workflow; quantify hours first; pick use cases on value, frequency, feasibility.

Train: hands-on, on the function's own work, not generic decks; prompting, context, verification, failure modes; champions per function; playbooks, prompt libraries, SOPs in English and Hindi.

Build: assistants, drafting, extraction from reports, requisitions, invoices, inspections, summarisation, search over internal knowledge, automations that end re-keying; integrate with ERP, maintenance, email, drives; live in weeks.

Govern: what data enters which tool; human in the loop on client submissions, safety, specs, payments, people; access, approvals, retention, audit trail, cost; own the AI policy.

Measure: baseline each deployment (hours, cycle time, rework, cost); report failures; retire the unused; ranked roadmap; track peers.

Requirements

Education: B.Tech in Computer Science or related engineering.

AI: LLMs in real deployments - prompting, context, retrieval, agents, evaluation. Demos do not count.

Software skillset (mandatory): Python; JavaScript; React and Node.js; SQL.

Foundations certificates (mandatory): Claude Certified Associate (CCAO-F), Developer (CCDV-F) and Architect (CCAR-F) - all Foundations.

Certifications (optional): Claude Certified Architect - Professional (CCAR-P); Microsoft Azure AI Engineer Associate (AI-102).

Teaching: sustained adoption among non-technical adults, not one-off workshops.

Domain: oil and gas, marine, heavy engineering or EPC preferred; safety-critical comfort beats pedigree.

Data: operational documents - reports, maintenance records, inspections, drawings, contracts.

Language: fluent English and Hindi.

Who Fits

Teaches rig hand to director plainly; sceptical of hype; baselines before claiming; says when the fix is the process; reads resistance as information; writes instructions that outlast the author; builds fast and small; careful with data; travels offshore; honest about failures.