ML Engineer I
Indexed description
This role is ideal for a highly experienced architect ready for their next step combining deep hands on expertise with strategic thinking, leadership, and enterprise collaboration.
Role and responsibilities
- Design and build platform components for an AI platform including core services, SDKs, client libraries, and reusable modules.
- Develop and maintain APIs (REST/gRPC) with clear versioning, backward compatibility, and OpenAPI/IDL specifications.
- Create and support CLIs and developer tools to simplify onboarding, testing, and deployment for internal and external users.
- Integrate features end to end: wire backend services to UI components, ensure consistent UX for platform features, and ship production ready front end changes.
- Implement SDKs and client libraries across languages, manage packaging, distribution, and semantic versioning.
- Build and operate data pipelines for ingestion, transformation, and feature preparation; ensure data quality, lineage, and monitoring.
- Collaborate with ML and DevOps teams to enable model training, serving, and lifecycle management; integrate with CI/CD and AIOps workflows.
- Ensure reliability and performance: write tests (unit, integration), benchmark services, and optimize latency and throughput.
- Document and evangelize APIs, SDKs, CLIs, and integration patterns; produce examples, tutorials, and SDK reference guides.
- Security and compliance: implement secure defaults, secrets handling, and input validation for APIs and SDKs.
- Continuous improvement: gather developer feedback, iterate on DX (developer experience), and reduce friction for platform consumers. Education and Experience
- Bachelor s or master degree in Computer Science, Information Technology, Engineering, or equivalent
- 5+ years of experience in building backend services, SDKs, APIs, and data pipelines; experience with AI/ML platforms or MLOps is highly desirable.
- Proven track record shipping production SDKs or client libraries and maintaining public or internal APIs.
- Experience integrating backend services with modern front ends and shipping UI features.
- Hands on experience with data engineering tools and production data workflows. Required Skills and Expertise
- Languages: Python; plus one or more of Go, Java, or TypeScript.
- API and SDK: REST, gRPC, OpenAPI; SDK design patterns; packaging and distribution (PyPI, npm).
- CLI development: experience building robust CLIs (e.g., Click, argparse, Cobra).
- Frontend integration: familiarity with React, Angular, or Vue for adding UI features and collaborating with frontend engineers.
- Data engineering: SQL, ETL patterns, Spark, Airflow, Kafka or other streaming systems, Parquet/Delta.
- Model serving and MLOps: exposure to model serving frameworks (Triton, TorchServe, KFServing) and model lifecycle concepts.
- Cloud and infra: AWS/GCP/Azure fundamentals; containerization with Docker; Kubernetes basics for deploying services.
- CI/CD and automation: Git, GitOps, Jenkins/GitHub Actions/GitLab CI, automated testing and release pipelines.
- Observability and testing: logging, metrics, tracing (Prometheus, Grafana, OpenTelemetry); strong testing discipline.
- Security and reliability: secrets management, input validation, rate limiting, and API security best practices.
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