Job description
Important Information
Location: Brazil
Job Mode: Full-time
Work Mode: Work from home
Job Summary
As an MLOps Engineer, you will play a key role in building and maintaining robust, scalable, and secure machine learning infrastructure to support the development and deployment of Large Language Models (LLMs) and chatbot solutions. You will collaborate closely with development engineers, data engineers, software developers, and other MLOps to enable fast, reliable, and traceable RAG workflows in production environments.
Responsibilities and Duties
- 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).
Essential Skills
- 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;
Highly Desirable Skills
- 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.
About Encora
Encora is the preferred digital engineering and modernization partner of some of the world’s leading enterprises and digital native companies. With over 9,000 experts in 47+ offices and innovation labs worldwide, Encora’s technology practices include Product Engineering & Development, Cloud Services, Quality Engineering, DevSecOps, Data & Analytics, Digital Experience, Cybersecurity, and AI & LLM Engineering.
At Encora, we hire professionals based solely on their skills and qualifications, and do not discriminate based on age, disability, religion, gender, sexual orientation, socioeconomic status, or nationality.
