Nervesparks Artificial Intelligence - Machine Learning - Deep Tech Job Description — Software Engineer (AI / ML) ROLE Software Engineer (AI/ML) DEPARTMENT Engineering — AI & ML LEVEL L1 / L2 (based on exp.) LOCATION In office — Delhi ABOUT NERVESPARKS Nervesparks is an AI-first technology company building intelligent systems and agentic frameworks that solve real-world problems at scale. We sit at the frontier of machine learning research and applied product engineering — the place where academic rigour meets production-grade deployment. Our teams are diverse, deeply technical, and driven by genuine intellectual curiosity. We are looking for engineers who think in models, build with intention, and care about the quality of what they ship. If you have spent your academic and early professional life going deeper on Data and AI/ML — not just using frameworks but understanding what is underneath them — Nervesparks is where that curiosity will be put to serious use. ROLE OVERVIEW As a Software Engineer (AI/ML) at Nervesparks, you will be responsible for the full lifecycle of machine learning and data solutions — from framing the problem and selecting the right approach, through model development, experimentation, evaluation, and deployment into production systems. You will work closely with research, product, and infrastructure teams, and your work will have direct, visible impact on the capabilities we deliver to clients and end users. This is not a role for someone who applies pre-built APIs and calls it machine learning. We expect you to understand your models, defend your design choices, and continuously improve what you build. WHAT YOU WILL DO › Design, develop, train, and evaluate machine learning and deep learning models across a range of problem domains — including but not limited to NLP,



computer vision, recommendation, and predictive analytics. › Translate business and product requirements into well-scoped ML problem statements, and lead the end-to-end development from data exploration through model deployment. › Work with large, complex, and often imperfect datasets — performing data engineering, feature engineering, and exploratory analysis as an integral part of model development. › Experiment rigorously — design experiments, track results, interpret findings, and communicate what worked, what didn't, and why. › Deploy models into production pipelines and maintain awareness of model performance over time, including monitoring, drift detection, and retraining. › Collaborate with peers on architecture decisions, code reviews, and technical design discussions — contributing to a high-quality engineering culture. › Stay current with developments in AI/ML research and bring relevant ideas from the literature into the team's applied work. › (Where applicable) Contribute to the organisation's research output — writing internal technical reports, and where work meets the bar, contributing to external publication. EDUCATIONAL QUALIFICATIONS Qualification Notes Required B.Tech in Artificial Intelligence & Machine Learning, Computer Science (AI/ML specialisation), or a closely related engineering discipline Must be from an accredited institution.



Coursework should demonstrate a strong foundation in mathematics, probability, linear algebra, and core ML theory. Preferred M.Tech in Artificial Intelligence, Machine Learning, Data Science, or a research-oriented postgraduate programme in a related field EXPERIENCE › Hands-on experience building, training, and evaluating machine learning models — whether in an industry role, internship, academic project, or personal research project. › Experience working with Python as the primary language for ML development. Proficiency with core libraries: NumPy, Pandas, Scikit-learn, and at least one deep learning framework (PyTorch strongly preferred; TensorFlow acceptable). › Familiarity with the ML development workflow: data preprocessing, feature engineering, model selection, hyperparameter tuning, evaluation, and iteration. › Experience working with real datasets — messy, incomplete, or large-scale data is a plus. › Exposure to model deployment or productionisation, even at a small scale, is valued. RESEARCH, PUBLICATIONS & PROJECTS Area What We Look For Why It Matters to Us Published Research Papers Peer-reviewed papers at recognised AI/ML conferences (NeurIPS, ICML, ICLR, CVPR, ACL, EMNLP, AAAI, or similar) or in reputable journals. Workshop papers, arXiv preprints, and co-authored student papers are also considered. Published research demonstrates the ability to formulate a problem rigorously, conduct systematic experiments, and communicate findings at a standard the field accepts. This is a strong differentiator. B.Tech / M.Tech Thesis Thesis quality often reveals depth of thinking that coursework grades cannot. We read theses. A solid thesis is weighted equivalently to industry experience at this level. ML / AI Projects E

📌 Software Engineer (Delhi)
🏢 Nerverks
📍 Delhi