Senior Platform Engineer with MLOps and Databricks

* 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.