Senior Machine Learning Engineer

Overview

Design, build, and deploy production-grade machine learning and AI systems as a Senior Machine Learning Engineer. Apply machine learning science, software engineering, and cross-disciplinary collaboration to turn complex scientific and business problems into scalable, reliable ML products.

What you'll do

• Design, build, and maintain scalable, production-grade machine learning systems and pipelines using CI/CD, testing, monitoring, and observability.
• Develop novel algorithms and models that are experimentally validated and deployed as reliable, scalable products.
• Build ML products using statistical modelling, deep learning, and AI techniques across operational, scientific, and R&D domains.
• Translate complex scientific and business problems into well-scoped ML solutions, actionable insights, and deployable capabilities.
• Architect and optimise ML systems for performance, scalability, and reliability in production environments.
• Collaborate with data scientists, data engineers, software engineers, and domain experts in cross-disciplinary teams.
• Advocate for engineering and data science best practices, including technical design, design reviews, unit testing, monitoring and alerting, code reviews, and documentation.
• Present technical results, trade-offs, and product outcomes to peers and senior customers.
• Improve developer velocity, engineering standards, and shared tooling.
• Mentor junior team members and contribute to the technical growth of the wider team.

What you'll need

• MSc or PhD degree in a quantitative field, such as Computer Science, Mathematics, Physics, Engineering, or a related discipline.
• Hands-on experience, typically 5+ years, designing, prototyping, productionising, maintaining, and scaling ML/data science products in complex environments.
• Strong and demonstrable expertise in machine learning algorithms, statistical modelling, and optimisation techniques, with a track record of applying these to build production-grade solutions.
• Applied knowledge of data science and ML tools across all stages of the data and model lifecycle.
• Thorough understanding of the mathematical foundations of statistics, machine learning, and scientific computing.
• Strong programming experience in one or more object-oriented languages, such as Python, Go, Java, or C++.
• Advanced SQL knowledge.
• Experience with modern ML engineering practices, including MLOps, model lifecycle management, CI/CD, and monitoring.
• Knowledge of experimental design, analysis, and scientific methodology.
• Customer-centric and pragmatic mindset focused on value delivery and swift execution while maintaining rigour and attention to detail.
• Strong stakeholder management and ability to influence across teams and organisations.
• Continuous learning and improvement mindset.

Nice to have

• Experience with big data technologies, such as Hadoop, Hive, or Spark.
• Experience with generative AI, LLMs, or retrieval-augmented generation (RAG).
• Exposure to Agentic AI concepts, including autonomous agents, tool use, and orchestration frameworks.
• Experience applying machine learning and AI to scientific or R&D workflows, with emphasis on building deployable ML products from scientific research, such as simulation, optimisation, or physics-informed models.
• Familiarity with model interpretability, uncertainty quantification, and sophisticated experimental methodologies.
• Proven record of publications, invention disclosures (IDFs), or patents in machine learning or AI.
• No prior experience in the energy industry required.

Details

• Location: Pune, India.
• Hybrid of office/remote working.
• Up to 10% travel should be expected.
• Role is eligible for relocation within country.
• Full-time position; flexible working arrangements may be considered.