Machine Learning Engineer - Azure & Databricks
RemoteColombia or Costa Rica
Job Summary
Deploy and operationalize Machine Learning and AI models while designing REST APIs to expose predictions and curated datasets. Build scalable batch and real-time inference pipelines, implementing model-serving patterns using microservices architectures. Develop low-latency systems supporting AI workloads and maintain data pipelines within Azure and Databricks environments. Productionize LLM-powered solutions, semantic search, and retrieval systems, then implement monitoring, logging, alerting, and CI/CD practices. Optimize performance, scalability, and cost efficiency of ML services and collaborate with Data Scientists and business stakeholders to deliver production-ready AI solutions.
Required Qualifications
- 5 to 8+ years of experience in Machine Learning Engineering or backend/platform engineering with ML exposure
- Strong programming skills in Python
- Experience building APIs using FastAPI, Flask, or similar frameworks
- Proven experience deploying Machine Learning models into production environments
- Experience building inference services and scalable APIs
- Knowledge of both batch and real-time Machine Learning systems
- Hands-on experience with Azure and Databricks or similar platforms
- Familiarity with Lakehouse architectures and distributed processing technologies such as Spark
- Experience designing RESTful APIs and scalable backend services
- Strong understanding of low-latency system design and microservices architecture
- Experience with CI/CD pipelines, Docker, and Kubernetes
- Knowledge of monitoring, observability, and DevOps best practices
Desired Qualifications
- Experience with LLM deployment and integration
- Knowledge of embeddings, semantic search, and Retrieval-Augmented Generation (RAG)
- Familiarity with Prompt Engineering workflows
- Exposure to insurance domains including policy, claims, or underwriting data
- Experience working in financial services or regulated industries
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