Senior Platform Engineer with MLOps and Databricks

Job Location: 100% remote in Romania

Recruitment process:

• HR Discussion
• Technical discussion - 1.5-hour 
• Client interview - 30 minutes

Role description:

• Design, implement, and maintain cloud-native platform to support AI and data workloads, with a focus on AI and data platforms such as Databricks and AWS Bedrock.
• Build and manage scalable data pipelines to ingest, transform, and serve data for ML and analytics.
• Develop infrastructure-as-code using tools like Cloudformation, AWS CDK to ensure repeatable and secure deployments.
• Collaborate with AI engineers, data engineers, and platform teams to improve the performance, reliability, and cost-efficiency of AI models in production.
• Drive best practices for observability, including monitoring, alerting, and logging for AI platforms.
• Contribute to the design and evolution of our AI platform to support new ML frameworks, workflows, and data types.
• Stay current with new tools and technologies to recommend improvements to architecture and operations.
• Integrate AI models and large language models (LLMs) into production systems to enable use cases using architectures like retrieval-augmented generation (RAG).

Qualifications:

• 5+ years of professional experience in software engineering and infrastructure engineering.
  Extensive experience building and maintaining AI/ML infrastructure in production, including model, deployment, and lifecycle management.
• Strong knowledge of AWS and infrastructure-as-code frameworks, ideally with CDK.
• Expert-level coding skills in TypeScript and Python building robust APIs and backend services.
• Production-level experience with Databricks MLFlow, including model registration, versioning, asset bundles, and model serving workflows.
• Expert level understanding of containerization (Docker), and hands on experience with CI/CD pipelines, orchestration tools (e.g., ECS) is a plus.
• Proven ability to design reliable, secure, and scalable infrastructure for both real-time and batch ML workloads.
• Ability to articulate ideas clearly, present findings persuasively, and build rapport with clients and team members.
• Strong collaboration skills and the ability to partner effectively with cross-functional teams.
  

Nice to have:

• Familiarity with emerging LLM frameworks such as DSPy for advanced prompt orchestration and programmatic LLM pipelines.
• Understanding of LLM cost monitoring, latency optimization, and usage analytics in production environments.
• Knowledge of vector databases / embeddings stores (e.g., OpenSearch) to support semantic search and RAG.