Machine Learning (ML) Scientist

About Netoyed

Netoyed (Grapes Telecom Pvt. Ltd.) is a CMMI Level 5 certified technology consulting company specializing in government digital transformation. With offices in Noida, Singapore, Sydney, the UAE, and the USA, the company serves major clients including ministries, constitutional bodies, banking institutions, and international organizations. Our platforms collectively serve over 130 million users.

What We Offer

Opportunity to work on nation-scale digital transformation projects impacting 130M+ citizens.

Exposure to cutting-edge technology stacks across cloud, AI/ML, and mobile platforms.

Collaborative work environment within a CMMI Level 5 organization with established engineering practices.

Career growth pathways across government, banking, and international organization projects.

Competitive compensation and benefits aligned with industry standards.

Department / Role

AI/ML

Designation

Machine Learning (ML) Scientist

Experience Required

5+ years

Employment Type

Full-Time, On-Site

Location

Noida, India

Position Summary

Lead the research, design, and development of advanced machine learning models and algorithms. Drive innovation in AI capabilities across country-scale platforms serving 130+ million users. Translate cutting-edge research into production-ready ML systems.

Key Responsibilities

Design, develop, and deploy production-grade machine learning models including deep learning, NLP, computer vision, and reinforcement learning pipelines.

Conduct rigorous research and experimentation to advance the state-of-the-art in applied ML for government digital transformation projects.

Architect scalable ML infrastructure on Azure and AWS, ensuring models perform reliably at scale (large scale user platforms).

Collaborate with cross-functional engineering, product, and data teams to define ML problem statements, success metrics, and delivery timelines.

Develop and maintain model training, evaluation, and monitoring frameworks with automated retraining and drift detection.

Author technical whitepapers, research documentation, and contribute to patent filings where applicable.

Mentor ML Analysts and Data Annotators, establishing coding standards, review processes, and knowledge-sharing practices.

Ensure compliance with government data handling standards including CERT-In, STQC, and Digital India data governance frameworks.

Evaluate and integrate emerging AI/ML tools, frameworks, and cloud-native AI services (Azure Cognitive Services, AWS SageMaker, etc.).

Present findings, model performance reports, and strategic recommendations to senior leadership and client stakeholders.

Develop model AWCs with end-to-end digitisation of Operations and Automation of Data Collection.

Supportive Supervision - Develop a Decision Support System for Audit, Recognition, MIS with data visualisation;

Required Qualifications

M.Tech/M.S./Ph.D. in Computer Science, Machine Learning, Statistics, Mathematics, or a closely related quantitative discipline.

5+ years of hands-on experience building and deploying ML models in production environments.

Deep expertise in Python, TensorFlow, PyTorch, scikit-learn, and at least one cloud ML platform (Azure ML, SageMaker).

Strong publication record or demonstrable contributions to applied ML research.

Proven experience with large-scale data processing using Spark, Dask, or equivalent distributed computing frameworks.

Solid understanding of MLOps, CI/CD for ML pipelines, model versioning, and experiment tracking (MLflow, Weights & Biases).

Experience with containerization (Docker, Kubernetes) and cloud-native deployment patterns for ML workloads.

Familiarity with government compliance and data sovereignty requirements is strongly preferred.

Preferred / Nice to Have

Experience with LLMs, Generative AI, RAG architectures, and fine-tuning foundation models.

Contributions to open-source ML projects or frameworks.

Experience working on large-scale platforms.

Knowledge of federated learning, edge ML, and privacy-preserving ML techniques.