Lead AI Engineer

Aditi Consulting; Chennai District; On-site; INR 3,000,000 - 5,200,000; Full time

Job summary

Aditi Consulting is seeking an experienced AI/ML engineer to design, develop, and deploy advanced AI/ML solutions for manufacturing and industrial products from concept to production.

You will mentor junior team members, collaborate across product, engineering, and operations, and apply Generative AI, MLOps, and scalable data pipelines to bring AI features to digital products.

Qualifications

• 5–10 years hands-on AI/ML engineering experience.
• Proven expertise in ML/DL, NLP, and Generative AI (LLMs, RAG, transformers).
• Strong Python programming; familiarity with Java/Go/Scala a plus.
• Experience deploying models with TensorFlow, PyTorch, and Transformers.
• Experience with MLOps, CI/CD for ML, and Git.
• Cloud deployment experience (AWS/GCP/Azure).
• Excellent cross-functional communication.

Responsibilities

• Architect and implement AI/ML models for automation and predictive analytics.
• Develop scalable data and ML pipelines from data processing to deployment.
• Collaborate with product, engineering, and operations to integrate AI features.
• Apply Generative AI for simulation, design, documentation, and operator assistance.
• Champion MLOps, code quality, and testing for robust deployments.
• Mentor junior engineers and data scientists.
• Stay current with AI advancements and adapt solutions.

Skills

AI/ML engineering

Deep Learning

NLP / Generative AI

Python

TensorFlow

PyTorch

Hugging Face Transformers

MLOps

Git

Cloud (AWS/GCP/Azure)

Docker

Kubernetes

Education

Masters in a relevant field

Tools

Docker

Kubernetes

Git

AWS

GCP

Azure

Hugging Face Transformers

Job description

You will design, develop, and deploy innovative AI/ML solutions for manufacturing and industrial products. A strong hands-on engineering approach, technical mentorship, and the ability to take projects from conception to deployment are expected. Cross-functional collaboration is key, but the position carries less strategic oversight and talent development responsibility than a Principal role.

Responsibilities:

• Architect and implement AI/ML models for automation, predictive analytics, and process optimization in manufacturing.
• Develop and maintain scalable data and ML pipelines; execute on end-to-end AI solution lifecycles (from data processing to model deployment and monitoring).
• Collaborate with product, engineering, and operations teams to design and integrate AI features into digital products.
• Apply Generative AI or LLMs for use cases in simulation, generative design, digital documentation, and intelligent operator assistance.
• Champion best MLOps practices, code quality, and testing for robust AI deployments.
• Mentor junior engineers and data scientists on technical best practices.
• Remain current with industry and AI advancements, and quickly adapt solutions to evolving needs.

Requirements:

• 5-10 years of hands-on AI/ML engineering (preferably in manufacturing, SaaS, or product companies).

Required Skills:

• Proven ability in Machine Learning, Deep Learning, Natural Language Processing (NLP), and/or Generative AI (LLMs, RAG, transformers).
• Experience deploying models with frameworks like TensorFlow, PyTorch, Hugging Face Transformers, etc.
• Strong programming skills in Python; familiarity with other languages or tools (Java, Go, Scala, Docker, K8s) is a plus.
• Experience with model training, fine-tuning, prompt engineering, and use of vector databases.
• Familiarity with cloud services (AWS/GCP/Azure) for model deployment.
• Experience with MLOps, CI/CD for ML, and version control (Git).
• Excellent communication and ability to work in a cross-functional team.

Preferred Skills:

• Experience with or exposure to digital twin tech in manufacturing (e.g., Nvidia Omniverse, Siemens, PTC); participate in integration/consumption rather than full-scale digital twin architecture.
• Knowledge of manufacturing data/standards (IoT, PLCs, MES/ERP connectivity).
• Masters in relevant fields, open-source contributions, or research publications.

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