Databricks MLOps Engineer

Solvd Inc. is a rapidly growing AI-native consulting and technology services firm delivering enterprise transformation across cloud, data, software engineering, and artificial intelligence. We work with industry-leading organizations to design, build, and operationalize technology solutions that drive measurable business outcomes. Following the acquisition of Tooploox, a premier AI and product development company, Solvd now offers true end-to-end delivery—from strategic advisory and solution design to custom AI development and enterprise-scale implementation. Our capability centers combine deep technical expertise, proven delivery methodologies, and sector-specific knowledge to address complex business challenges quickly and effectively. We are looking for a Databricks MLOps Engineer to design, build, and operate cloud-native infrastructure that powers AI and data workloads at scale. You'll work at the intersection of platform engineering and AI — building the pipelines, deployment workflows, and observability layers that make machine learning models reliable, cost-efficient, and production-ready. This is a hands-on engineering role. You'll be writing infrastructure-as-code, building data pipelines, integrating LLMs into production systems, and collaborating closely with AI and data engineers to keep the platform performing as the workloads grow.

Responsibilities

Design, implement, and maintain a cloud-native platform to support AI and data workloads, with a focus on Databricks and AWS Bedrock; Build and manage scalable data pipelines to ingest, transform, and serve data for ML and analytics use cases; Develop infrastructure-as-code using CloudFormation and AWS CDK to ensure repeatable, secure deployments; Integrate AI models and LLMs into production systems, including RAG architectures and model serving workflows; Drive observability best practices — monitoring, alerting, and logging across AI platforms; Collaborate with AI engineers, data engineers, and platform teams to improve performance, reliability, and cost-efficiency of models in production; Contribute to the design and evolution of the AI platform to support new ML frameworks, workflows, and data types; Stay current with emerging tooling and recommend improvements to architecture and operations

Requirements

7+ years of professional experience in software and infrastructure engineering; Extensive experience building and maintaining AI/ML infrastructure in production — model deployment, lifecycle management, and serving workflows; Production-level experience with Databricks and MLflow — model registration, versioning, asset bundles, and serving; Strong knowledge of AWS and infrastructure-as-code, with AWS CDK experience specifically; Expert-level coding skills in both Python and TypeScript — building robust APIs and backend services; Strong understanding of containerization with Docker and hands-on CI/CD pipeline experience; Proven ability to design reliable, secure, and scalable infrastructure for real-time and batch ML workloads; Strong communication and collaboration skills — able to articulate technical decisions clearly to both engineering and non-technical stakeholders

Nice to have

Familiarity with DSPy or similar LLM orchestration frameworks for programmatic prompt pipelines; Experience with LLM cost monitoring, latency optimization, and usage analytics in production; Knowledge of vector databases and embedding stores — OpenSearch or similar — to support semantic search and RAG architectures; Experience with ECS or other container orchestration tools

Benefits

Shape real-world AI-driven projects across key industries, working with clients from startup innovation to enterprise transformation; Be part of a global team with equal opportunities for collaboration across continents and cultures; Thrive in an inclusive environment that prioritizes continuous learning, innovation, and ethical AI standards