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.