Lead ML Engineer – Classical ML & GenAI/RAG

Accountabilities:: Design, develop, and productionize machine learning solutions that address real-world business problems. Build and optimize supervised and unsupervised ML models across classification, regression, forecasting/time-series, clustering, and anomaly detection use cases. Own the end-to-end ML lifecycle, including data preparation, feature engineering, model development, validation, tuning, evaluation, deployment, and ongoing improvement. Establish appropriate baselines and evaluate model performance against measurable business outcomes. Develop reusable, production-quality Python and SQL code and maintain automated training and inference pipelines. Apply software engineering best practices, including unit and integration testing, Git-based version control, code reviews, and reliable documentation. Identify and prevent data leakage and other common modeling issues that could affect model reliability or validity. Deploy and support ML models in production environments, taking ownership of monitoring, troubleshooting, versioning, reproducibility, retraining, and operational performance. Work across cloud and on-premise environments where required and contribute to scalable ML deployment approaches. Review existing ML codebases, establish reliable performance baselines, identify improvement opportunities, and implement measurable enhancements. Translate ambiguous business and technical requirements into practical ML solutions and production-ready code. Collaborate with data scientists, software engineers, product teams, business stakeholders, and other technical groups. Provide hands-on technical leadership, mentoring, and guidance to ML engineers and data scientists. Develop and support GenAI, LLM, and RAG-based applications alongside classical ML solutions. Implement evaluation and debugging approaches for RAG and LLM applications, including retrieval and model performance assessment. Continuously improve the performance, reliability, scalability, maintainability, and operational effectiveness of AI/ML solutions. Requirements 5+ years of hands-on experience in Machine Learning, Data Science, ML Engineering, or a closely related field. Strong practical experience with classical machine learning and proven experience delivering supervised and/or unsupervised ML solutions into production. Strong Python programming and SQL skills, with experience writing reusable, production-quality code. Hands-on experience developing and maintaining ML training and inference pipelines. Strong understanding of data preparation, feature engineering, model validation, data leakage prevention, hyperparameter tuning, model evaluation, and baseline comparison. Demonstrated experience deploying and supporting ML models in production, including monitoring, troubleshooting, versioning, reproducibility, and retraining. Experience with Git/version control, code reviews, testing, and software engineering best practices. Ability to work directly within an existing codebase, investigate technical challenges, and deliver working solutions independently. Recent hands-on experience developing GenAI, LLM, and RAG applications. Experience evaluating and debugging RAG and LLM-based solutions. Strong problem-solving, analytical, communication, collaboration, and technical leadership skills. Ability to work effectively with ambiguous requirements and translate business needs into practical technical solutions. Experience deploying ML solutions across cloud and on-premise environments is an asset. Familiarity with ML/MLOps platforms and tooling is preferred. Experience with Docker, Kubernetes, or similar deployment technologies is a plus. Experience with AWS, Azure, GCP, or other major cloud platforms is beneficial. Experience with model serving and API-based ML deployment is an advantage. Familiarity with LLM evaluation frameworks, RAG architectures, embeddings, vector databases, retrieval pipelines, and prompt/model evaluation is desirable. Bachelor's or Master's degree in a relevant technical or quantitative discipline. Benefits Full-time, fully remote position based in India. Opportunity to work on production-grade Machine Learning and AI solutions with real-world business applications. Hands-on technical leadership role with direct ownership across the ML development and production lifecycle. Opportunity to work with both classical ML and emerging GenAI, LLM, and RAG technologies. Exposure to cloud and on-premise environments and modern ML/MLOps practices. Opportunity to mentor and provide technical guidance to other ML engineers and data scientists. Collaborative environment involving engineering, data science, product, and business stakeholders. Opportunity to contribute to scalable, reliable, and continuously improving AI solutions. Bachelor’s or Master’s degree accepted, with 5–10 years of relevant professional experience.