Lead AI ML Engineer

Netscribes; Maharashtra; On-site; INR 300,000 - 500,000; Full time

Job summary

Netscribes is seeking a senior ML leader to own end-to-end CV, NLP, and GenAI solutions, driving model development, deployment, and monitoring. You will lead a pod of ML engineers, mentored through reviews and best practices, and collaborate with clients to translate AI outcomes into tangible business value.

The role requires deep hands-on experience with PyTorch/TensorFlow, LLMs, MLOps, and scalable cloud deployment, with a focus on responsible AI checks and evaluation.

Qualifications

• Bachelor's or Master's degree in Computer Science, ML, Data Science, or a related field.
• 8+ years in software/data roles, including 5+ years building ML systems and 2+ years leading engineers.
• Expert-level Python and strong software eng. fundamentals.
• Hands-on with PyTorch and/or TensorFlow.
• Proven delivery in CV (detection/segmentation/OCR) and NLP with transformers (Hugging Face).
• Experience with LLMs, fine-tuning, prompt engineering, and RAG.
• Strong MLOps with pipelines, registries, Docker, and Kubeflow.
• Cloud deployment on AWS/SageMaker, Azure ML, or Google Vertex AI.
• Excellent communication to explain trade-offs to clients.
• Publications, patents, or open-source in CV/NLP/Applied ML.
• Experience at scale with Kubernetes serving and vector databases.
• Edge or on-device inference and model optimization (quantisation/distillation).
• Domain experience in market intelligence, healthcare, retail, manufacturing, or financial services.

Responsibilities

• Own the design and delivery of CV/NLP/GenAI solutions end-to-end.
• Build/optimize CV models for image/video tasks and OCR/document understanding.
• Develop NLP solutions including text classification and semantic search.
• Design and deliver GenAI applications with prompts, fine-tuning, and retrieval-augmented generation.
• Establish robust MLOps, reproducible pipelines, model versioning, and CI/CD.
• Deploy scalable, low-latency services on cloud platforms (AWS/Azure/GCP).
• Collaborate with data engineering on data pipelines and labeling strategies.
• Lead and mentor ML engineers and data scientists; code/design reviews.
• Track advances in CV/NLP/GenAI and apply state-of-the-art techniques.
• Engage with clients to scope use cases and translate AI outcomes to business value.
• Operate MLOps platforms at scale, including Kubernetes-based serving.

Skills

Python

Deep learning

NLP

CV

Pytorch

TensorFlow

LLMs

MLOps

Cloud platforms

Communication

Leadership

Multimodal models

Edge inference

Vector databases

Kubernetes

Education

Bachelor's or Master's in CS/ML/DS

Tools

OpenCV

Docker

MLflow

Kubeflow

Git

Job description

Responsibilities

• Technical leadership: Own the design and delivery of CV and NLP solutions end-to-end, from problem framing and feasibility through model development, deployment, and monitoring.
• Computer Vision: Build and optimise models for image and video classification, object detection, segmentation, OCR and document understanding, and visual search using modern deep-learning architectures.
• Natural Language Processing: Develop solutions for text classification, named-entity recognition, summarisation, semantic search, and conversational AI using transformer models.
• LLMs and Generative AI: Design and deliver applications built on large language models, including prompt engineering, fine-tuning, retrieval-augmented generation (RAG), and evaluation of GenAI outputs.
• MLOps and productionization: Establish robust MLOps practices, reproducible training pipelines, model versioning, CI/CD for models, automated testing, monitoring, and drift detection.
• Scalable deployment: Deploy models as scalable, low-latency services on cloud platforms (AWS, Azure, GCP), including containerised and, where needed, edge or GPU-optimized inference.
• Data and evaluation: Partner with data engineering on data pipelines and labelling strategy, and define rigorous evaluation, benchmarking, and responsible-AI checks for every model.
• Client engagement: Work directly with clients and solution architects to scope use cases, set realistic expectations, present results, and translate AI outcomes into business value.
• Team leadership and mentoring: Lead, coach, and grow a pod of ML engineers and data scientists; run code and design reviews; and uphold engineering standards and best practices.
• Applied research: Track advances in CV, NLP, and GenAI, run focused experiments, and bring proven techniques into Netscribes' delivery and innovation work.

Requirements

• Bachelor's or Master's Degree in Computer Science, Machine Learning, Data Science, or a related field.
• 8+ years in software/data roles, including 5+ years building machine learning systems and 2+ years leading or mentoring engineers.
• Expert-level Python and strong software engineering fundamentals: testing, version control, code review, and clean, maintainable code.
• Deep hands-on experience with modern deep-learning frameworks such as PyTorch and/or TensorFlow.
• Proven delivery in Computer Vision detection, segmentation, classification, or OCR using libraries and frameworks such as OpenCV and current detection architectures.
• Proven delivery in NLP using transformer models and the Hugging Face ecosystem.
• Practical experience with LLMs and Generative AI, including fine-tuning, prompt engineering, and retrieval-augmented generation.
• Strong MLOps experience with pipelines, model registries, containerization (Docker), and tools such as MLflow, Kubeflow, or equivalents.
• Experience deploying ML on at least one major cloud platform (AWS SageMaker, Azure ML, or Google Vertex AI).
• Excellent communication skills and the ability to explain technical trade-offs to non-technical and client stakeholders.
• Publications, patents, or open-source contributions in CV, NLP, or applied ML.
• Experience operating MLOps and ML platforms at scale, including Kubernetes-based serving.
• Hands‑on work with vector databases and large-scale semantic search.
• Experience with multimodal models that combine vision and language.
• Exposure to edge or on-device inference and model optimisation (quantisation, distillation, pruning).
• Domain experience in market intelligence, healthcare, retail, manufacturing, or financial services.
• Education: Graduates only (please ignore pursuing/drop out/12th or 10th pass).