Machine Learning Lead

Role & responsibilities

• Lead the design, development, deployment, and optimization of machine learning and AI solutions for complex business problems.
• Own the ML strategy, technical roadmap, architecture, and end-to-end delivery of high-impact machine learning initiatives.
• Lead a team of Data Scientists, ML Engineers, and Analysts, providing technical direction, mentoring, and code/model reviews.
• Translate business requirements into scalable machine learning models, algorithms, and production-ready solutions.
• Develop and optimize models across classification, regression, recommendation systems, NLP, computer vision, forecasting, and Generative AI, as applicable.
• Drive the complete ML lifecycle including data preparation, feature engineering, model development, validation, deployment, monitoring, and retraining.
• Design scalable ML pipelines, MLOps frameworks, model serving platforms, and monitoring systems.
• Collaborate with Product, Engineering, Data Engineering, Analytics, and Business teams to identify and prioritize ML opportunities.
• Evaluate and adopt emerging AI/ML technologies, frameworks, algorithms, and tools to improve model performance and business outcomes.
• Establish best practices for model development, experimentation, reproducibility, testing, deployment, governance, and responsible AI.
• Monitor model performance, data quality, drift, scalability, and production reliability.
• Drive experimentation and A/B testing to measure the effectiveness and business impact of ML solutions.
• Lead technical discussions, architecture reviews, and design decisions related to ML systems.
• Build and maintain production-grade ML solutions using cloud platforms, distributed computing, APIs, and modern data infrastructure.
• Communicate complex technical concepts, model performance, business impact, and recommendations to senior stakeholders.

Preferred candidate profile

• 5 to 10 years of experience in Machine Learning, Data Science, Artificial Intelligence, or a related field, with demonstrated technical leadership experience.
• Strong hands-on expertise in Machine Learning, Deep Learning, Statistical Modeling, Predictive Analytics, and AI.
• Advanced proficiency in Python and SQL; experience with R, PySpark, or Scala is an added advantage.
• Strong experience with Scikit-learn, TensorFlow, PyTorch, Keras, XGBoost, LightGBM, Pandas, and NumPy.
• Strong understanding of machine learning algorithms, statistics, probability, optimization, feature engineering, model evaluation, and experimentation.
• Proven experience building and deploying production-grade ML models and scalable ML systems.
• Strong knowledge of MLOps, MLflow, model deployment, model monitoring, CI/CD, Docker, Kubernetes, and model lifecycle management.
• Hands-on experience with at least one major cloud platform: AWS, Azure, or GCP.
• Experience with Apache Spark, Databricks, Snowflake, data pipelines, ETL, and large-scale data processing is preferred.
• Experience in Generative AI, LLMs, RAG, NLP, recommendation systems, computer vision, or time-series modeling is an added advantage.
• Strong understanding of ML system design, distributed systems, APIs, scalability, performance optimization, and production architecture.
• Proven ability to lead, mentor, and develop Data Scientists and ML Engineers.
• Strong stakeholder management and ability to work effectively with Product, Engineering, Data, and Business teams.
• Excellent problem-solving, analytical, communication, technical leadership, and decision-making skills.
• Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Mathematics, Statistics, Engineering, or a related quantitative discipline.
• M.Tech/M.E./M.Sc./MCA or Ph.D. in a relevant field is an added advantage.