AI-Enabled Design Engineer

Santriya Technologies; Chennai District; On-site; INR 900,000 - 1,500,000; Full time

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.

You will convert requirements into concepts, drawings, BOMs and specifications, explore alternatives with AI, and validate results using engineering methods, ensuring accuracy and adherence to approved processes.

Qualifications

• Experience using AI tools for design exploration and validation.
• Ability to translate requirements into detailed drawings, BOMs and specs.
• Familiarity with design-for-manufacture/assembly/maintenance concepts.

Responsibilities

• Convert requirements and constraints into concepts, calculations, drawings, BOMs and specifications.
• Use AI tools to explore alternatives, summarize constraints and flag issues, then validate with engineering methods.
• Perform design reviews, tolerance checks, DFMEA and manufacturability assessments.
• Manage design changes and maintain revision-controlled documentation.
• Coordinate with procurement, manufacturing, quality and maintenance teams.
• Evaluate vendor data against approved requirements.
• Support prototype/site validation and capture lessons into standards.

Skills

AI tools

DFMEA

Design for manufact/assembly

Tools

AI copilots

Enterprise AI platforms

Job description

• 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 escalate 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