We are looking for a Senior MLOps Engineer to design, build, and operate the infrastructure that enables machine learning models and large language models to be deployed safely, reliably, and at scale.
We are looking for a Senior MLOps Engineer to design, build, and operate the infrastructure that enables machine learning models and large language models to be deployed safely, reliably, and at scale. In this role, you will create the end-to-end capabilities required to move models from experimentation into production, expose them through secure and highly available endpoints, and enable users and applications to interact with AI-powered services. You will work across AWS and Databricks to establish robust CI/CD pipelines, model-serving infrastructure, observability, governance, rollback mechanisms, and operational standards. You will partner closely with data scientists, machine learning engineers, software engineers, security teams, and platform engineers. The adecuado candidate combines strong cloud and DevOps engineering skills with a practical understanding of machine learning systems, LLM deployment patterns, and production reliability.
Key Responsibilities MLOps Platform and Architecture Design and implement a scalable MLOps platform using AWS and Databricks.
Define reference architectures and reusable deployment patterns for traditional machine learning models, deep learning models, and large language models.
Build standardized workflows that move models from development and validation into staging and production.
Develop self-service capabilities that allow data scientists and ML engineers to deploy models without manually managing infrastructure.
Establish clear separation between development, testing, staging, and production environments.
Design multi-region or multi-availability-zone architectures where required by business continuity and availability objectives.
CI/CD and Model Deployment Build automated CI/CD pipelines for model code, inference services, infrastructure, configuration, and model artifacts.
Implement automated testing across the deployment lifecycle, including: Unit testing
Integration testing
Model validation
Data contract validation
API and endpoint testing
Security testing




Performance and load testing
Regression testing

Automate model packaging, containerization, versioning, approval, promotion, and deployment.
Support deployment strategies such as blue-green deployments, canary releases, shadow deployments, and controlled traffic shifting.
Implement reliable rollback and roll-forward mechanisms for application code, infrastructure, model versions, prompts, and configuration.
Ensure deployments are reproducible, auditable, and recoverable.
Model and LLM Serving Design and operate secure, scalable, low-latency inference endpoints.
Deploy models using appropriate services and patterns across AWS and Databricks, such as: Databricks Model Serving
MLflow Model Registry
Amazon SageMaker
Amazon ECS or EKS
AWS Lambda, where appropriate
API Gateway
Application Load Balancers

Build synchronous, asynchronous, batch, and streaming inference capabilities.
Design serving architectures for LLM-powered applications, including: Hosted foundation models
Open-source models
Fine-tuned models
Retrieval-augmented generation
Embeddings services
Vector search
Prompt and response orchestration
Tool-calling and agentic workflows

Optimize inference performance, scalability, GPU utilization, concurrency, throughput, latency, and cost.
Implement autoscaling, request throttling, queuing, caching, timeout handling, and graceful degradation.
Reliability, Recovery, and Business Continuity Build recoverable model-serving endpoints with clearly defined recovery time and recovery point objectives.
Implement automated health checks, failover mechanisms, retry policies, circuit breakers, and service recovery procedures.
Design backup and recovery processes for: Model artifacts
Model registry metadata
Feature definitions
Deployment configurations
Infrastructure state
Prompts and application configuration




Vector indexes and knowledge-base assets

Create disaster recovery procedures and regularly test restoration and failover capabilities.
Ensure production services can recover from failed deployments, infrastructure outages, model errors, and upstream dependency failures.
Develop operational runbooks and incident response procedures.
Monitoring and Observability Implement end-to-end observability for infrastructure, applications, models, data, and user interactions.
Monitor: Availability
Request volume
Latency
Error rates
Resource utilization
Model performance
Data quality
Data drift
Concept drift
Prediction distributions
LLM response quality
Hallucination and safety indicators
Token consumption
Cost per request

Establish dashboards, alerts, service-level indicators, and service-level objectives.
Integrate monitoring with incident management and on-call processes.
Enable traceability from user requests through model inference, retrieval, orchestration, and downstream services.
Support root-cause analysis by maintaining structured logs, metrics, traces, model lineage, and deployment history.
Security and Governance Implement security controls for model-serving environments, APIs, data access, and deployment pipelines.
Apply least-privilege access using AWS IAM, Databricks permissions, service principals, and role-based access control.
Secure secrets, credentials, API keys, certificates, and tokens using approved secrets-management solutions.
Implement encryption in transit and at rest.
Design private networking, endpoint controls, firewall rules, and secure connectivity patterns.
Support authentication, authorization, rate limiting, and tenant isolation for AI services.
Ensure models and LLM applications comply with organizational requirements for privacy, security, auditability, and responsible AI.
Maintain model lineage, approval records, version history, and deployment audit trails.
Implement controls for sensitive data, personally identifiable information, prompt injection, unsafe outputs, and unauthorized model access.
Infrastructure as Code and Automation Build and

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📌 Senior Machine Learning Engineer (Centro)
🏢 HP..
📍 Centro