Lead Machine Learning Engineer

Visionet Systems; Bengaluru; Hybrid; INR 4,000,000 - 7,000,000; Full time

Job summary

Visionet Systems in Bengaluru seeks a Lead Machine Learning Engineer to design, develop, deploy, and optimize production-ready ML/DL solutions for intelligent document processing and automation.

You will drive NLP, Computer Vision, Transformer-based models, and Generative AI initiatives, mentor engineers, and shape architecture decisions for enterprise-scale deployments.

Qualifications

• Bachelor's or Master's degree in a related technical field.
• 4+ years of professional ML/AI experience.
• Strong Python and software engineering fundamentals.
• Hands-on experience with PyTorch or TensorFlow.
• Experience with NLP, CV, Transformers, and LLMs.

Responsibilities

• Design, develop, train, evaluate, and deploy ML/DL solutions for intelligent document processing and automation.
• Build and optimize NLP, Computer Vision, Transformer-based models, and Large Language Models (LLMs).
• Design and implement Generative AI and RAG pipelines using embeddings, vector search, and hybrid retrieval.
• Develop production-ready ML inference services using Python, FastAPI, and Flask.
• Integrate ML models with enterprise applications, APIs, databases, storage systems, queues, and downstream services.
• Monitor production ML systems for latency, throughput, error rates, cache hits, GPU utilization, and model performance.
• Lead technical discussions, architecture decisions, code reviews, and mentor engineers.

Skills

Python

NLP

Computer Vision

Deep Learning

Transformers

LLMs

Mentoring

Software Engineering

Problem Solving

Education

Bachelors or Masters in CS/AI/Data Science/Engineering

Tools

PyTorch

TensorFlow

Transformers library

FastAPI

Flask

Docker

Azure

Kafka

REST APIs

Job description

Role Overview

We are looking for a Lead Machine Learning Engineer to design, develop, deploy, and optimize production-ready machine learning and deep learning solutions. The role focuses on intelligent document processing and automation, NLP, Computer Vision, Transformer-based models, and Generative AI applications, including Retrieval-Augmented Generation (RAG). The successful candidate will also guide technical implementation, make architecture decisions, and mentor engineers.

Roles & Responsibilities

• Design, develop, train, evaluate, and deploy machine learning and deep learning solutions for intelligent document processing and automation.
• Build and optimize solutions using NLP, Computer Vision, Transformer-based models, and Large Language Models (LLMs).
• Design and implement Generative AI and RAG pipelines using embeddings, vector search, hybrid retrieval, reranking, and LLM/VLM-based generation.
• Develop production-ready ML inference services using Python, FastAPI, and Flask.
• Integrate ML models with enterprise applications, APIs, databases, storage systems, queues, and downstream services.
• Optimize ML inference performance through batching, asynchronous processing, caching, retry mechanisms, exponential backoff, and efficient GPU utilization.
• Monitor production ML systems and analyze latency, throughput, error rates, cache hit rates, GPU utilization, and model performance.
• Troubleshoot production issues related to model accuracy, inference performance, GPU memory, service reliability, and data quality.
• Lead technical discussions, architecture decisions, code reviews, and technical implementation of ML initiatives.
• Mentor engineers and provide technical guidance on machine learning, deep learning, and Generative AI solutions.
• Lead Machine Learning Engineer | Bengaluru, Hybrid

Required Skills & Qualifications

• Bachelors or Masters degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Engineering, or a related technical field.
• 4+ years of professional experience in Machine Learning, Artificial Intelligence, Data Science, or a related domain.
• Strong proficiency in Python and a good understanding of software engineering principles.
• Strong understanding of machine learning, deep learning, NLP, and Computer Vision fundamentals.
• Hands-on experience with PyTorch and/or TensorFlow and the Transformers ecosystem.
• Strong understanding of Transformer architectures, BERT-based models, attention mechanisms, embeddings, and model fine tuning.
• Experience with LLMs, VLMs, Generative AI, prompt engineering, embeddings, and Retrieval-Augmented Generation (RAG).
• Hands-on experience with FastAPI and/or Flask and REST API development.
• Experience with Docker and GPU-based ML inference.
• Experience with cloud platforms, preferably Microsoft Azure.
• Familiarity with distributed/asynchronous processing systems such as Kafka, Azure Service Bus, or Azure Queue.
• Understanding of production ML monitoring, performance optimization, and system reliability.
• Strong analytical, problem-solving, communication, and collaboration skills.
• Experience leading technical initiatives and mentoring engineers is preferred.