AI-Enabled Design Engineer

Santriya Technologies; Chennai; On-site; INR 900,000 - 1,300,000; Full time

Job summary

Santriya Technologies Ltd. is seeking an AI-enabled Design Engineer to develop safe, manufacturable and maintainable designs using approved AI/generative design tools.

You will translate requirements into concepts, calculations, drawings, BOMs and specifications with human accountability for engineering decisions. In this role you will validate AI outputs against established docs, perform DFMEA and design-for-manufacture assessments, and coordinate with procurement, manufacturing, quality and

Responsibilities

• Convert requirements and product/facility constraints into concepts, calculations, drawings, BOMs and specifications.
• Use AI/generative tools to explore alternatives, summarize constraints and flag potential issues, then validate using engineering methods.
• Perform design reviews, tolerance/interface checks, DFMEA and design-for-manufacture/assembly/maintenance assessments.
• Manage technical queries, design changes and revision-controlled documentation.
• Coordinate with procurement, manufacturing, construction, quality and maintenance teams.
• Evaluate vendor data and component selections against approved requirements.
• Support prototype/site validation and capture lessons into standards.
• Protect proprietary drawings and technical data by using only approved AI environments.

Job description

Job Summary

Santriya Technologies is seeking an AI-Enabled Design Engineer to develop safe, manufacturable and maintainable designs using approved AI/generative design tools for option generation and review, with calculations and engineering approval remaining human-accountable.

Product & documentation operating model

You will be assigned one or more company products, client solutions or operational services. The approved product documentation, SOPs, policies, process maps, knowledge articles, release notes, controls, scripts and AI runbooks are the source of truth. Use only approved enterprise AI tools to retrieve, summarize, draft, classify, prioritize or automate work. Validate material AI outputs against current documentation and authoritative system data, record exceptions, and escalation conflicts or missing guidance. Do not invent product features, prices, customer entitlements, engineering limits, regulatory positions or safety instructions.

Key Responsibilities

• Convert requirements and product/facility constraints into concepts, calculations, drawings, BOMs and specifications.
• Use AI/generative tools to explore alternatives, summarize constraints and flag potential issues, then validate using engineering methods.
• Perform design reviews, tolerance/interface checks, DFMEA and design-for-manufacture/assembly/maintenance assessments.
• Manage technical queries, design changes and revision-controlled documentation.
• Coordinate with procurement, manufacturing, construction, quality and maintenance teams.
• Evaluate vendor data and component selections against approved requirements.
• Support prototype/site validation and capture lessons into standards.
• Protect proprietary drawings and technical data by using only approved AI environments.

AI-enabled ways of working

• Use approved GenAI copilots, enterprise search/RAG, analytics and workflow agents to reduce repetitive work and improve decision preparation.
• Keep prompts, outputs and automated actions within approved data-access, confidentiality and retention rules.
• Correct inaccurate summaries, classifications or recommendations before they become customer, operational or system records.
• Feed recurring AI errors, knowledge gaps and process friction into product, documentation and control improvement.

Human accountability

AI is a copilot, not the accountable decision-maker. The role holder remains responsible for judgement, quality, privacy, security and appropriate escalation. Credit, fraud blocking, legal/regulatory, financial advice, safety-critical, clinical, employment and other high-impact decisions must follow delegated authority and required human review.

Success Measures

• Design cycle time improves without quality loss
• First-pass design approval improves
• Change/rework rates decline
• Design knowledge becomes reusable and controlled