Databricks MLOps Engineer (AI)

Location: Remote from Argentina, Brazil, Colombia, Georgia, Poland, or Ukraine

Company

hirify.global is an AI-native consulting and technology services firm delivering cloud, data, software engineering, and artificial intelligence solutions for enterprise transformation.

What you will do

• Design, build, and operate a cloud-native platform for AI and data workloads using Databricks and AWS Bedrock.
• Build scalable data pipelines for machine learning and analytics use cases.
• Develop secure, repeatable infrastructure deployments with CloudFormation and AWS CDK.
• Integrate AI models and LLMs into production systems, including RAG architectures and model serving workflows.
• Implement monitoring, alerting, logging, and observability practices across AI platforms.
• Collaborate with AI engineers, data engineers, and platform teams to improve production performance, reliability, and cost efficiency.

Requirements

• 7+ years of professional experience in software and infrastructure engineering.
• Production experience with AI/ML infrastructure, model deployment, lifecycle management, and serving workflows.
• Hands-on Databricks and MLflow experience, including model registration, versioning, asset bundles, and serving.
• Strong AWS and infrastructure-as-code knowledge, including AWS CDK.
• Expert coding skills in Python and TypeScript, including robust APIs and backend services.
• Experience with Docker, CI/CD pipelines, and reliable infrastructure for real-time and batch machine learning workloads.

Nice to have

• Experience with DSPy or similar LLM orchestration frameworks.
• Experience with LLM cost monitoring, latency optimization, and usage analytics.
• Knowledge of vector databases and embedding stores such as OpenSearch for semantic search and RAG.
• Experience with ECS or other container orchestration tools.

Culture & Benefits

• Work on AI-driven projects ranging from startup innovation to enterprise transformation.
• Collaborate with a global team across continents and cultures.
• Inclusive environment focused on continuous learning and innovation.
• Commitment to responsible and ethical AI standards.