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Lead Software Platform Engineer, MLOps

TetraScience· Engineering
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Remote$200k - $270kfull-timePosted 2mo ago

About the role

Lead Software Platform Engineer at the intersection of distributed systems and MLOps. Owns and scales AI and data infrastructure as a product, architecting cloud-based services and MLOps infrastructure to enable production-grade AI/ML workflows for customers and internal teams, with a focus on multi-tenant safety, cost, latency, and regulatory compliance in pharma environments.

Responsibilities

  • Own the technical architecture of the AI/ML platform: the service and API surface our customers use to run models and agents against their own scientific data, and that Applied AI and data engineering teams build against internally.
  • Own the end-to-end model and prompt lifecycle across Databricks MLflow and AWS Bedrock, including registration, versioning, asset bundles, staged promotion, rollback, and multi-model serving.
  • Design the inference substrate for both real-time and batch AI workloads, including routing, batching, caching, concurrency control, GPU and accelerator capacity planning, handling of large binary inputs such as instrument images, and graceful degradation under load.
  • Integrate AI models and large language models (LLMs) into production systems using architectures like retrieval-augmented generation (RAG), and architect the agentic layer above them: tool and function calling, MCP-based tooling, and agent runtimes, deciding what belongs in the platform versus in the applications built on top of it.
  • Design security into the AI platform, partnering with security on guardrails, prompt-injection and tool-abuse defenses, PII/PHI handling, and hard tenant data boundaries across prompts, retrieval, and tool calls.
  • Build the evaluation and quality infrastructure: offline/online eval harnesses, golden datasets, regression gates in CI, A/B and shadow deployment, drift/hallucination detection.
  • Establish observability: monitoring, alerting, logging, distributed tracing, and SLI/SLO/SLA for probabilistic outputs.
  • Design for reproducibility and lineage in a validated pharma environment with versioned data, code, prompts, and model artifacts plus audit trails for regulatory scrutiny.
  • Contribute to infrastructure-as-code and deployment automation (CloudFormation, AWS CDK) for multi-tenant infra, online upgrades, and on-demand compute allocation.
  • Own production readiness for the AI platform with engineers and platform teams, focusing on performance, reliability, and cost-efficiency, plus incident response and runbooks.
  • Act as SME and design authority across product and engineering; lead design reviews, write reference architectures and technical docs, and mentor engineers.
  • Set technical direction on emerging AI infrastructure, evaluate new frameworks, serving runtimes, model providers, and data types; make build-versus-buy decisions grounded in cost, risk, and scalability.

Qualifications

  • 10+ years of professional experience in software engineering and infrastructure engineering, with a track record designing, building, and scaling distributed, cloud-native systems in production.
  • Experience as a technical leader or architect responsible for system design, scalability, performance, and cost optimization.
  • Experience designing security into a multi-tenant platform, including tenant boundaries and handling of PII/PHI, with awareness of prompt injection and tool abuse risks.
  • Extensive experience building and maintaining AI/ML infrastructure in production as a multi-tenant product with external users.
  • Hands-on experience taking LLM-based systems to production, including RAG architecture, retrieval/embedding design, prompt/model versioning, and tool/function calling.
  • Expert-level coding skills in TypeScript and Python for robust APIs and backend services.
  • Production-level experience with a model registry and serving stack (ideally Databricks MLflow).
  • Experience treating AI evaluation as a release gate with eval harnesses, regression gates, and drift/quality monitoring.
  • Proficiency in API-first design (REST, OpenAPI) with scalable, secure, versioned APIs.
  • Solid knowledge of AWS, containerization (Docker), and infrastructure-as-code (CloudFormation or CDK); ability to contribute to CI/CD.
  • Experience defining observability and SLI/SLO/SLA practices for production systems.
  • Clear communication skills to articulate ideas to customers and cross-functional teams; ability to influence non-manager teams and mentor engineers.

Skills

Required:Architectural design of distributed systemsDatabricks MLflowAWS BedrockTypeScript and PythonSecurity design for multi-tenant platformsObservability and SRE practices

Benefits

  • 100% employer-paid benefits for all eligible employees and immediate family members
  • Unlimited paid time off (PTO)
  • 401K
  • Flexible working arrangements - Remote work
  • Company paid Life Insurance, LTD/STD
  • A culture of continuous improvement with coaching
Last updated Oct 21, 2026. Interested? Apply on the company site.
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About TetraScience

The only R&D data cloud for life sciences

TetraScience provides the world’s first and only R&D Data Cloud, with a mission to transform life sciences R&D, accelerate discovery, and improve human life. Scientists at global pharma and biotech organizations rely on our innovative Tetra Data Platform for easy access to centralized, harmonized, and actionable scientific data to accelerate their digital lab transformation. With best-in-class SaaS performance, a team of industry innovators, and excellent product/market fit, Tetra is positioned to become an iconic life sciences software company.

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