Role Snapshot

JOB TITLE : Lead AI / ML Engineer - Computer Vision & NLP

FUNCTION / PILLAR : Engineering & Innovation - Data Science & AI

LOCATION : Hybrid - Mumbai / Pune

EXPERIENCE : 10+ years overall, including 7+ years building ML systems and 3+ years leading engineering teams

EMPLOYMENT TYPE : Full-time, Permanent

TEAM : Leads a pod of ML engineers and data scientists

Role Overview :

We are looking for a Lead AI / ML Engineer with deep expertise in Computer Vision and NLP to anchor our applied AI delivery practice. This is a senior individual-contributor-plus-leadership role : you will own complex, high-stakes AI initiatives end to end - from scoping and architecture through delivery, deployment, and ongoing model health - while building and mentoring the engineers around you.

You bring the technical depth to make hard architectural calls, the ownership mindset to drive initiatives across ambiguity, and the communication clarity to align clients and internal stakeholders on progress and trade-offs. You are equally comfortable whiteboarding a new RAG pipeline and reviewing a PR for production readiness.

Beyond individual projects, you will help define how Netscribes builds and ships AI - setting engineering standards, shaping our MLOps practice, and contributing applied research that feeds back into client delivery. If you thrive on accountability, like to move fast without cutting corners, and want to work on genuinely hard ML problems across industries, this role is for you.

Key Responsibilities :

- Technical ownership : Own the design and delivery of CV and NLP solutions end to end - from problem framing and feasibility through model development, deployment, and monitoring. You are accountable for the outcome, not just your slice of it.

- 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 & Generative AI : Design and deliver applications built on large language models, including prompt engineering, fine-tuning, retrieval-augmented generation (RAG), and rigorous evaluation of GenAI outputs.

- MLOps & productionisation : Establish and enforce 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-optimised inference.

- Data & evaluation : Partner with data engineering on data pipelines and labelling strategy; 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 & mentoring : Lead, coach, and grow a pod of ML engineers and data scientists; run code and design reviews; shepherd projects from kickoff to production; 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.

Required Qualifications :

- Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science, or a related field.

- 10+ years in software or data roles, including 7+ years building production machine learning systems and 3+ years leading or mentoring engineering teams.

- Demonstrated ability to take full ownership of initiatives - driving work from brief to shipped product with minimal direction and a clear bias for action.

- 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 - pipelines, model registries, containerisation (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.

Preferred Qualifications :

- 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.

What We Offer :

- High-impact scope : End-to-end ownership of AI initiatives across a diverse portfolio of enterprise clients, with direct visibility to outcomes.

- Technical community : A peer group of ML engineers, data scientists, and domain experts committed to craft and continuous learning.

- Applied research culture : Dedicated time and support to run experiments, explore new methods, and contribute to Netscribes' innovation agenda.

- Flexibility : Hybrid work model based in Mumbai or Pune, with a standard 5-day week.

- Growth : A clear path from senior engineering leadership into practice-building and client advisory as the AI team scales.