Open role
AI Engineer (GenAI) – Manufacturing
Review the role, confirm interview capacity, and move into the candidate flow from a single professional workspace.
- Company
- Saffroot Technologies LLP
- Location
- Hyderabad
- Employment type
- Fulltime
- Experience
- 3-6
- Salary / budget
- Not specified
- Date posted
- 2026-08-06
- Notice period
- 30 days
- Skills
- AL/ML, Data Pipeline Engineering, Cloud Platforms
Role overview
Job description
Tech Mahindra-Hyderabad-AI Engineer (GenAI) – Manufacturing Job Description Role Overview We are looking for AI Engineers (Junior & Senior) to develop and deploy Generative AI-driven solutions in a manufacturing environment. The role focuses on intelligent automation, engineering productivity, and digital thread transformation across product lifecycle, supply chain, and operations. Key Responsibilities - Develop, test, and deploy AI/ML and Generative AI solutions (LLMs, RAG pipelines) - Process and prepare structured and unstructured manufacturing data - Integrate AI models into enterprise systems (PLM, ERP, MES, IoT) - Build data pipelines and feature engineering workflows - Monitor model performance and optimise accuracy - Collaborate with engineering, IT, and operations teams - Ensure scalable deployment using MLOps/GenAIOps - Maintain compliance with AI ethics and governance Junior AI Engineer (0–3 Years) Responsibilities: - Support ML/GenAI model development - Perform data preprocessing and labelling - Build proof-of-concepts - Assist in testing and debugging Skills: - Python programming - Basic ML/DL understanding - Familiarity with TensorFlow/PyTorch - Exposure to LLM tools Senior AI Engineer (5–10+ Years) Responsibilities: - Lead enterprise-scale AI/GenAI solution design - Architect end-to-end AI systems - Implement LLM-based solutions (RAG, fine-tuning) - Define AI roadmap aligned to manufacturing transformation - Mentor junior engineers Skills: - Advanced ML/DL/GenAI expertise - LLMs, vector databases, RAG pipelines - MLOps and cloud platforms (AWS/Azure/GCP) - Enterprise-scale AI deployment Industry Use Cases - Engineering assistants - Document intelligence (BOM, specs, compliance) - Predictive maintenance - Supply chain forecasting - Quality inspection using computer vision Education & Experience Bachelor’s or Master’s degree in Computer Science, AI, Data Science, or Engineering. Manufacturing domain experience preferred. Success Metrics - Deployment of production-grade AI solutions - Improved operational efficiency - Reduced cost and cycle time - Adoption across engineering and operations