Lead ML Engineer Classical ML and GenAI OR RAG

Job Title: Lead ML Engineer Classical ML & GenAI/RAG
About the Role

We are looking for a hands-on Lead ML Engineer to take ownership of developing, deploying, and supporting predictive AI/ML solutions in production.

The ideal candidate will have a strong foundation in classical Machine Learning/Data Science, with substantial hands-on experience building ML solutions before the recent GenAI wave, and should have subsequently expanded their expertise into Generative AI, LLM, and RAG-based applications.

This role requires someone who can work independently through ambiguous business and technical requirements, contribute directly to the codebase, build reliable ML pipelines, and take ownership of models through production deployment and ongoing improvement.

The primary focus is classical ML implementation and production delivery. GenAI/RAG experience is an important complementary skill, but this is not an architecture-only, advisory, or prompt-engineering role.

Key Responsibilities

• Design, develop, and productionize machine learning solutions for real-world business problems.
• Build supervised and unsupervised ML models across areas such as:
• Classification
• Regression
• Forecasting/time-series
• Clustering
• Anomaly detection
• Perform end-to-end data preparation, feature engineering, model development, validation, tuning, and evaluation.
• Establish appropriate baselines and evaluate models against measurable business outcomes.
• Develop reusable and production-quality Python and SQL code.
• Build and maintain automated training and inference pipelines.
• Implement appropriate unit/integration testing, version control, code reviews, and engineering best practices.
• Identify and prevent data leakage and other common modeling issues.
• Deploy and support ML models in production environments.
• Take ownership of model reproducibility, versioning, monitoring, troubleshooting, and retraining.
• Work with both cloud and on-premise environments where required.
• Analyze existing ML codebases, establish reliable baselines, identify improvement opportunities, and implement measurable enhancements.
• Translate ambiguous requirements into practical ML solutions and working production code.
• Collaborate with data scientists, engineers, product/business stakeholders, and other technical teams.
• Provide hands-on technical leadership and guidance to other ML engineers/data scientists.
• Develop and support GenAI/LLM/RAG applications, building on a strong classical ML foundation.
• Implement evaluation and debugging approaches for RAG and LLM-based solutions.
• Continuously improve model performance, reliability, scalability, and maintainability.

Required Skills & Experience

Must Have:

• 5+ years of hands-on experience in Machine Learning/Data Science/ML Engineering or closely related roles.
• Strong practical experience with classical machine learning.
• Proven experience developing supervised and/or unsupervised ML solutions in production.
• Strong Python programming skills.
• Strong SQL skills.
• Experience developing production-quality, reusable code.
• Experience with ML training and inference pipelines.
• Strong understanding of:
• Data preparation
• Feature engineering
• Model validation
• Data leakage prevention
• Hyperparameter tuning
• Model evaluation
• Baseline comparison
• Hands-on experience deploying and supporting ML models in production.
• Experience with model monitoring, troubleshooting, versioning, reproducibility, and retraining.
• Experience with Git/version control and code reviews.
• Ability to work hands-on within an existing codebase and deliver working solutions.
• Recent hands-on experience with GenAI/LLM/RAG applications.
• Experience evaluating and debugging RAG/LLM solutions.
• Strong problem-solving and technical leadership capabilities.

Good to Have

• Experience deploying ML solutions across cloud and on-premise environments.
• Experience with ML/MLOps platforms and tooling.
• Experience with Docker/Kubernetes or similar deployment technologies.
• Experience with cloud platforms such as AWS, Azure, or GCP.
• Experience with model serving and API-based ML deployment.
• Experience with LLM evaluation frameworks and RAG architectures.
• Experience working with embeddings, vector databases, retrieval pipelines, and prompt/model evaluation