Senior Machine Learning Engineer

Company Description TD Newton is a technology-focused organization dedicated to solving complex real-world problems through data-driven innovation and advanced engineering. The company emphasizes collaborative problem-solving, modern software practices, and the responsible use of machine learning in production environments. Team members work across disciplines to build scalable systems and models that deliver measurable business impact. TD Newton values continuous learning, technical excellence, and an inclusive workplace where diverse perspectives contribute to better solutions.
Role Description The Senior Machine Learning Engineer role at TD Newton is a full-time, hybrid position based in Gurugram, with flexibility for partial work from home. In this role, the engineer designs, develops, and deploys machine learning models, focusing on end-to-end ownership from data exploration and feature engineering through production integration and monitoring. Day-to-day responsibilities include collaborating with product and engineering teams to identify opportunities for ML solutions, implementing robust algorithms, optimizing model performance, and ensuring reliability, scalability, and security of ML pipelines. The role also involves reviewing code, mentoring other engineers, establishing best practices for experimentation and evaluation, and contributing to architectural decisions for data and model infrastructure. The engineer is expected to document solutions clearly, communicate findings to technical and non-technical stakeholders, and stay current with emerging techniques in machine learning and software engineering.
Qualifications
Strong foundation in Computer Science concepts, including data structures, algorithms, and software engineering practices.
Expertise in Pattern Recognition and Neural Networks, with experience designing, training, and evaluating models for real-world applications.
Solid grounding in Statistics, including hypothesis testing, probability, and statistical modeling for data analysis and model evaluation.
Practical experience implementing and optimizing Algorithms for large-scale data processing and machine learning workloads.
Proficiency in one or more programming languages commonly used in ML (such as Python, Java, or C++), and experience with ML frameworks (such as TensorFlow, PyTorch, or scikit-learn).
Experience building and deploying ML models into production environments, including familiarity with APIs, microservices, and MLOps tools.
Background working with cloud platforms and data infrastructure (such as AWS, GCP, Azure, or similar), and tools for data pipelines and orchestration.
Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, Mathematics, or a related technical field, or equivalent practical experience.
Ability to communicate complex technical concepts clearly, collaborate effectively in cross-functional teams, and provide technical leadership or mentorship.
Experience in designing experiments, A/B testing, and performance monitoring for ML systems is highly beneficial.

Work arrangement
Hybrid