MLOps Engineer

Summary

Join Encora as an MLOps Engineer and build and maintain robust, scalable, and secure machine learning infrastructure for Large Language Models (LLMs) and chatbot solutions. Collaborate with engineers and other MLOps to enable fast, reliable, and traceable RAG workflows in production. Work with structured and unstructured data from various sources. Build scalable and reusable MLOps frameworks, translating complex workflows into automated pipelines. Support LLM-based pipelines, particularly RAG architectures. This is a full-time, work-from-home position located in Brazil.

Requirements

• Strong proficiency in Python and shell scripting for automation of ML workflows
• Solid understanding of CI/CD pipelines tailored for machine learning, covering model training, validation, deployment, and monitoring
• Proficiency with containerization technologies like Docker and Kubernetes for model serving and orchestration
• Solid understanding of ETL/ELT workflows and data pipeline architecture
• Experience with model versioning, feature stores, and experiment tracking tools
• Familiarity with cloud platforms (AWS, Azure, or GCP) and infrastructure-as-code tools (e.g., Terraform, CloudFormation)
• Edge deployment and architecture knowledge
• Familiarity with vector databases and retrieval logic to support LLM integration in production environments

Responsibilities

• Working with structured and unstructured data, including ingestion from APIs, databases, and data lakes
• Hands-on experience with RAG workflows, including embedding generation, document chunking, and vector similarity search
• Exposure to monitoring and alerting systems for ML models in production (e.g., Prometheus, Grafana, Zabbix)
• Building scalable, maintainable, and reusable MLOps frameworks
• Translate complex ML and RAG workflows into automated, observable pipelines with clear operational SLAs
• Supporting LLM-based pipelines, particularly RAG architectures (e.g., vector stores like FAISS, Pinecone, Weaviate)

Preferred Qualifications

• Knowledge of ML security and governance practices (e.g., model explainability, access control, auditability)
• Experience with LLMs in production, including prompt engineering, fine-tuning, and retrieval optimization
• Familiarity with LangChain, LlamaIndex, or other RAG orchestration frameworks

Benefits

Work from home