Machine Learning Engineer
Core
Design, build, and operate platforms, pipelines, and reusable patterns to take machine learning and Generative AI models from experimentation to production at scale for insurance, banking, and wealth management.
Role type
Senior Machine Learning Engineer (MLOps & GenAI)
Builds
Production-grade ML and GenAI platforms, serving infrastructure, and reusable data/ML patterns
Domain
Financial Services (Insurance, Banking, Wealth Management) + MLOps/GenAI
Deliverable
production ML models
Required skills
Python, ML frameworks, MLOps/LLMOps, CI/CD, Terraform, Docker, Kubernetes, Azure Key Vault, model lifecycle management, data pipeline optimization, model monitoring
Preferred skills
Java/Scala, LangChain/LangGraph/OpenAI SDK, Spark, Databricks (Delta/Unity Catalog), RAG, vector search, model fine-tuning
Technologies
Python, MLflow, Azure Machine Learning, Databricks, Jenkins, GitHub Actions, Azure DevOps, Terraform, Docker, Kubernetes, Azure Key Vault, LangChain, LangGraph, OpenAI SDK, Spark, Delta, Unity Catalog
Responsibilities
Build reusable patterns for data, ML, and GenAI workloads following MLOps/LLMOps best practices; Own CI/CD pipelines for ML delivery including source control, build/deployment, and automated testing; Provision and manage PaaS infrastructure using Terraform; Implement secure credential handling using Azure Key Vault; Develop scalable ML platforms and serving infrastructure; Partner with data engineers to build high-quality feature and training pipelines; Design, train, evaluate, and deploy ML models and integrate LLMs; Monitor and improve models for accuracy, latency, cost, and drift; Ensure responsible AI through governance, security-by-design, and privacy controls; Partner with stakeholders to integrate ML solutions; Stay current with emerging technologies like RAG and vector search.
Seniority
Senior, hands-on IC
