Lead Machine Learning Engineer - LLM/RAG
First seen by Alion on Nov 14, 2026.
Role Overview :
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.
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, and anomaly detection.
- Perform end-to-end data preparation, feature engineering, model development, validation, tuning, and evaluation.
- Develop reusable and production-quality Python and SQL code.
- Build and maintain automated training and inference pipelines.
- Deploy and support ML models in production environments, ensuring reproducibility, versioning, and monitoring.
- 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.
Required Skills & Experience :
- 5+ years of hands-on experience in Machine Learning/Data Science/ML Engineering.
- Strong practical experience with classical machine learning and production deployment.
- Strong Python and SQL programming skills.
- Experience with ML training and inference pipelines.
- Experience with Git/version control and code reviews.
- Recent hands-on experience with GenAI/LLM/RAG applications.
- Strong problem-solving and technical leadership capabilities.
Good to Have :
- Experience with cloud platforms (AWS, Azure, GCP) and on-premise environments.
- Experience with MLOps platforms, Docker/Kubernetes, and model serving.
- Experience with LLM evaluation frameworks and RAG architectures.
Skills
Machine Learning, Data Science, Python, SQL, Generative AI, LLM, RAG, Model Validation, MLOps