Lead Software Platform Engineer (MLOps)

Join our team as a Lead Software Platform Engineer (MLOps) and take ownership of our AI and data infrastructure. You will architect cloud-based services and MLOps infrastructure, working closely with Applied AI engineers, data engineers, and platform teams. Your work will have a significant impact on the scientific community, as the capabilities you build will be used by scientists at the world's largest pharmaceutical companies. If you thrive on turning ambitious scalability and cost targets into concrete technical strategies, we want to hear from you.

Architect and scale AI and data infrastructure as a product for customers.

About TetraScience

USD 200,000 - 270,000

Responsibilities

• Own the technical architecture of the AI/ML platform, including the service and API surface.
• Design and implement the end-to-end model and prompt lifecycle across Databricks MLflow and AWS Bedrock.
• Expert-level coding skills in TypeScript and Python building robust APIs and backend services, with the judgment to critically evaluate AI-generated code for correctness, security, and architectural fit
• Solid working knowledge of AWS and containerized workloads (e.g., Docker), and familiarity with infrastructure-as-code such as CloudFormation or CDK, with the ability to contribute to CI/CD pipelines and deployment automation
• Demonstrated experience as a technical leader or architect, accountable for the key decisions on system design, scalability, performance, and cost optimization
• Production-level experience with a model registry and serving stack, ideally Databricks MLflow, including model registration, versioning, asset bundles, and serving workflows

Requirements

• Experience defining observability and SLI/SLO/SLA practice for production systems, including monitoring, alerting, and distributed tracing
• Deep, hands-on experience taking LLM-based systems to production, including RAG architecture, retrieval and embedding design, prompt and model versioning, and tool or function calling. Not just prototyping with an SDK. We are looking for someone who has operated these systems under real traffic, real latency budgets, and real failure modes
• Experience treating AI evaluation as a release gate rather than post-hoc reporting, including eval harnesses, regression gates, and drift or quality monitoring for non-deterministic systems
• Extensive experience building and maintaining AI/ML infrastructure in production, including model deployment and lifecycle management, delivered as a multi-tenant product with external users rather than internal tooling for a single team. Candidates whose experience is limited to building pipelines for their own team will not be a fit
• Proficiency in API-first design, including REST and OpenAPI specifications, designing APIs that are scalable, secure, versioned, and extensible
• 10+ years of professional experience in software engineering and infrastructure engineering, with a proven track record of designing, building, and scaling distributed, cloud-native systems in production
• Experience designing security into a multi-tenant platform, including authorization boundaries between tenants and handling of sensitive data such as PII and PHI, with awareness of LLM-specific risks like prompt injection and tool abuse
• Ability to articulate ideas clearly to customers and cross-functional teams, influence technical direction on teams you do not manage, and mentor other engineers
• Familiarity with emerging LLM frameworks for advanced prompt orchestration and programmatic LLM pipelines
• Understanding of LLM cost monitoring, latency optimization, and usage analytics in production environments, including per-tenant cost attribution for tokens, GPU, and inference
• Experience with multimodal model inputs, including image and instrument data, and the throughput and cost implications of serving them at scale
• Background in scientific, life sciences, or laboratory data domains

Preferred

• Experience running agentic or multi-step orchestration in production, and with MCP as an integration surface for tooling and non-human identities
• Experience with fine-tuning, distillation, or model optimization such as quantization, batching, or KV-cache strategy, to improve latency and cost
• Experience delivering ML or AI systems in a regulated or validated environment (GxP, 21 CFR Part 11, SOC 2), including computer system validation and audit readiness