Machine Learning Engineer

Job Title : Senior Full-Stack AI / ML Engineer – Vision Analytics, LLMs & Hybrid Deployment

Location : Pune from KBL Baner Pune Office

Department : Corporate Information Centre (CIC)

Role Overview

As a Senior Full-Stack AI / ML Engineer at KBL, you will play a key role in developing and deploying intelligent systems that enhance operational efficiency, quality assurance, and customer experience. You will work on a range of use cases from vision-based quality inspection and predictive maintenance to LLM-driven document understanding and automation .

This role requires strong technical depth, independent ownership, and the ability to collaborate with cross-functional teams across engineering, IT, and business domains.

Key Responsibilities

Lead the design, development, and deployment of AI / ML models across vision, language, and prediction use cases

Develop and implement solutions using LLMs (e.g., LLaMA, Azure OpenAI) and Computer Vision frameworks (OpenCV, PyTorch, YOLO, etc.)

Architect hybrid deployment models using Azure cloud and on-premises infrastructure , ensuring high availability and scalability

Collaborate with internal stakeholders using design thinking to convert business needs into AI solutions

Develop end-to-end ML pipelines (ETL, training, evaluation, inference, monitoring)

Optimize performance of models for real-time inference and resource efficiency (GPU / CPU)

Ensure best practices in CI / CD, containerization (Docker / Kubernetes), and model lifecycle management

Support internal teams with documentation, training, and handover of AI tools and platforms

Required Qualifications & Experience

B.Tech from a premier engineering institute ; M.Tech preferred

5+ years of relevant experience in AI / ML engineering and production deployments

Proficiency in Python , PyTorch , and deep learning tools

Strong experience with LLMs (e.g., LLaMA, GPT) and vision analytics for object detection, classification, and OCR

Proven ability to deploy models on Azure and on-prem environments with GPU acceleration

Familiarity with predictive modelling techniques in manufacturing / engineering contexts

Excellent communication and stakeholder engagement skills

Desirable Skills (Good to Have)

Experience with digital twin models , edge computing , or smart factory systems

Knowledge of Hugging Face , vLLM , or other open-source LLM frameworks

Exposure to MLOps tools , API development (FastAPI), and automated model retraining

Awareness of data security , model interpretability , and ethical AI practices

Soft Skills & Role Readiness

Candidate should be self-driven and able to work independently with minimal guidance

Strong verbal and written communication skills to engage with business and technical teams

Ability to translate requirements into technical solutions using design thinking principles

Comfortable with working in a manufacturing / industrial environment if required onsite

Supplier Responsibilities

Conduct a technical screening and validation before sharing profiles.

Provide reference checks and past project verification (especially for AI / ML deployment experience)

Take ownership of resource onboarding , handover coordination , and periodic performance feedback collection and assistance

Provide a backfill plan in case of early attrition or performance misalignment