What are the responsibilities and job description for the Machine Learning Engineer position at Evlo AI?
About The Role
The role owns the end-to-end machine learning lifecycle — designing, training, and deploying models that run in production and support high-stakes decision-making at scale. This is a hands-on engineering position, not a research bench: models built here are shipped, monitored, and iterated on continuously.
The team works across classic ML and modern LLM-based systems, partnering closely with data engineers, platform teams, and product stakeholders to turn prototypes into reliable, measurable systems where latency, accuracy, and cost all matter.
Key Responsibilities
The role owns the end-to-end machine learning lifecycle — designing, training, and deploying models that run in production and support high-stakes decision-making at scale. This is a hands-on engineering position, not a research bench: models built here are shipped, monitored, and iterated on continuously.
The team works across classic ML and modern LLM-based systems, partnering closely with data engineers, platform teams, and product stakeholders to turn prototypes into reliable, measurable systems where latency, accuracy, and cost all matter.
Key Responsibilities
- Design, train, and evaluate ML models — supervised learning, NLP, and LLM-based pipelines — for production workloads with measurable business impact
- Build and maintain scalable data and feature pipelines in Python and SQL, ensuring consistency between training and serving environments
- Deploy and manage models in production using Docker, Kubernetes, and cloud platforms (AWS, GCP, or Azure), including CI/CD, versioning, and rollback strategies
- Implement monitoring and alerting for model drift, data quality issues, and performance degradation; drive root-cause analysis and retraining decisions
- Develop evaluation frameworks and experiments (A/B tests, offline benchmarks) to validate model improvements before release
- Fine-tune and integrate LLMs where appropriate — RAG architectures, prompt engineering, and parameter-efficient fine-tuning — with rigorous evaluation
- Contribute to engineering standards through code reviews, documentation, and shared tooling
- 3–6 years of experience in machine learning engineering, applied ML, or MLOps, with multiple production deployments
- Strong Python skills and deep proficiency with at least one framework: PyTorch, TensorFlow, or scikit-learn
- Hands-on experience with MLOps tooling: MLflow, Airflow, Docker, Kubernetes, and a major cloud ML stack (AWS SageMaker, GCP Vertex AI, or Azure ML)
- Solid ML fundamentals: model selection, evaluation metrics, regularization, and the practical tradeoffs of training vs. serving
- Experience engineering robust data pipelines and working with both structured and unstructured data at scale
- MS or BS in Computer Science, Statistics, Engineering, or a related quantitative field (or equivalent practical experience)
- Bonus: Experience deploying LLM applications (RAG, fine-tuning, vector search), expertise with Spark or streaming systems, or contributions to open-source ML tooling