Senior Software Platform Engineer, MLOps

TetraScience; Cambridge; On-site; GBP 128,000 - 166,000; Full time

Benefits offered by this job

Competitive compensation

Stock options

Employer-paid benefits for all

401(k)

Unlimited PTO

Flexible working arrangements

Life Insurance

Job summary

TetraScience is building the scientific data and AI cloud for biopharma. We are seeking a Senior Software Platform Engineer who works at the intersection of distributed systems and MLOps to build and run key parts of our AI and data infrastructure.

You will own major parts of the AI platform end to end, collaborating with Applied AI engineers, data engineers, and platform teams to deliver cloud services behind production-grade AI/ML workflows.

Qualifications

• 7+ years of professional experience in software engineering and infrastructure engineering.
• Experience taking ownership of a significant service or component, including design decisions on scalability, performance, and cost.
• Hands‑on experience building and maintaining AI/ML infrastructure in production, including model deployment and lifecycle management.
• 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.
• Experience building security into a multi-tenant platform, including authorization boundaries and handling of PII/PHI.
• Strong coding skills in TypeScript and Python for building reliable APIs and backend services.
• Production experience with a model registry and serving stack, ideally Databricks MLflow.
• Experience treating AI evaluation as a release gate with eval harnesses and drift monitoring.
• Experience designing APIs with REST and OpenAPI.
• CI/CD, CloudFormation or CDK, and containerized workloads (Docker).
• Observability, SLI/SLO, monitoring, and distributed tracing.

Responsibilities

• Design, build, and operate services and APIs for models and agents against scientific data.
• Own the model and prompt lifecycle across Databricks MLflow and AWS Bedrock.
• Build inference path for real-time and batch AI workloads including routing and batching.
• Integrate LLMs into production systems using retrieval-augmented generation (RAG).
• Implement security measures with guardrails, data boundaries, and threat defenses for multi-tenant use.
• Develop evaluation tooling: offline/online eval harnesses, regression gates, drift detection.
• Set up monitoring, alerting, logging, and distributed tracing with meaningful SLIs/SLOs.
• Ensure reproducibility with versioned data, code, prompts, and model artifacts with audit trails.
• Write infrastructure-as-code (CloudFormation, CDK) for multi-tenant infra and on-demand compute.
• Own production readiness, reliability, and cost optimization; participate in design reviews and mentoring.

Skills

Distributed systems

MLOps

TypeScript

Python

REST APIs

OpenAPI

Databricks MLflow

AWS Bedrock

Docker

CloudFormation

CDK

Security

Observability

Model registry

Tools

Databricks MLflow

AWS Bedrock

Docker

CloudFormation

AWS CDK

Job description

TetraScience is building the scientific data and AI cloud for biopharma. The platform is the innermost foundation for developing, delivering and operating our enterprise grade, secure, compliant Scientific Data and AI capabilities customers rely on.

We are looking for a Senior Software Platform Engineer who works at the intersection of distributed systems and MLOps. You will build and run key parts of our AI and data infrastructure, which our customers build their science on and which every other engineering team builds against. You will work closely with Applied AI engineers, data engineers, and platform teams to deliver the cloud services and MLOps infrastructure behind production-grade AI/ML workflows. You will own major parts of the AI platform end to end, from design through production. You will work with the Lead Platform Engineer and Applied AI teams on how the pieces fit together, and help shape the platform’s direction through design reviews. The work covers the model and prompt lifecycle, evaluation and observability, tenant and data boundaries around prompts and retrieval, and the cost and latency controls that keep production AI economical at scale.

This is a high-impact seat for an engineer who has shipped AI/ML infrastructure that real external users depend on, not only internal tooling. The model serving, MCP, and agent capabilities you build are used directly by scientists at the world's largest pharmaceutical companies, running against their own data under their compliance obligations. If that scope appeals to you, and you want to turn ambitious scalability and cost targets into working systems inside a regulated environment, we'd love to talk to you.

What You Will Do

• Design, build, and operate the services and APIs that let customers run models and agents against their own scientific data, and that Applied AI and data engineering teams build against internally.
• Own the model and prompt lifecycle in your area across Databricks MLflow and AWS Bedrock, including registration, versioning, asset bundles, staged promotion, rollback, and multi-model serving.
• Build the inference path for real-time and batch AI workloads, including routing, batching, caching, concurrency control, capacity planning, handling of large binary inputs such as instrument images, and graceful degradation under load.
• Integrate LLMs into production systems using retrieval-augmented generation (RAG), and build agent capabilities such as tool and function calling, MCP-based tooling, and agent runtimes.
• Build the security into the platform with our security team, including guardrails, prompt-injection and tool-abuse defenses, PII and PHI handling, and hard tenant data boundaries across prompts, retrieval, and tool calls.
• Build the evaluation and quality tooling that makes AI shippable: offline and online eval harnesses, golden datasets, regression gates in CI, A/B and shadow deployment, and drift and hallucination detection in production.
• Set up monitoring, alerting, logging, and distributed tracing for the services you own, and define SLIs and SLOs that make sense for systems whose outputs are probabilistic.
• Build for the reproducibility and lineage a validated pharma environment requires: versioned data, code, prompts, and model artifacts, with an audit trail that holds up to customer and regulatory review.
• Write infrastructure-as-code and deployment automation (CloudFormation, AWS CDK) for the services you own, working with the team that owns production deployments to support multi-tenant infrastructure, online upgrades, and on-demand compute.
• Own production readiness for your services with Applied AI engineers, data engineers, and platform teams: performance, reliability, and cost of models in production, plus incident response and runbooks.
• Take part in design reviews, write the design docs and technical documentation for your components, and mentor mid-level engineers.
• Evaluate new frameworks, serving runtimes, and model providers, and bring clear build-versus-buy recommendations.

• 7+ years of professional experience in software engineering and infrastructure engineering, with a track record of building and running distributed, cloud-native systems in production.
• Experience taking technical ownership of a significant service or component, including design decisions on scalability, performance, and cost.
• Hands‑on experience building and maintaining AI/ML infrastructure in production, including model deployment and lifecycle management, for users outside your own team. Candidates whose experience is limited to building pipelines for their own team will not be a fit.
• 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. Prototyping with an SDK is not enough. We are looking for someone who has operated these systems under real traffic, real latency budgets, and real failure modes.
• Experience building 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 such as prompt injection and tool abuse.
• Strong coding skills in TypeScript and Python for building reliable APIs and backend services, with the judgment to review AI-generated code for correctness, security, and fit.
• Production experience with a model registry and serving stack, ideally Databricks MLflow, including model registration, versioning, asset bundles, and serving workflows.
• Experience treating AI evaluation as a release gate rather than after-the-fact reporting, including eval harnesses, regression gates, and drift or quality monitoring for non-deterministic systems.
• Experience designing APIs with REST and OpenAPI that are scalable, secure, versioned, and extensible.
• Working knowledge of AWS and containerized workloads (for example Docker), and familiarity with infrastructure-as-code such as CloudFormation or CDK, with the ability to contribute to CI/CD pipelines and deployment automation.
• Experience with observability and SLI/SLO practice for production systems, including monitoring, alerting, and distributed tracing.
• Clear communication with customers and cross‑functional teams, and the ability to influence technical direction on teams you do not manage.

• Competitive compensation
• Stock options in a VC‑backed Series C company
• 100% employer‑paid benefits for all eligible employees and immediate family members
• 401(k)
• Unlimited paid time off (PTO)
• Flexible working arrangements
• Company‑paid Life Insurance, LTD/STD

No visa sponsorship is available for this position

The salary range for this position is $170,000-$220,000 USD. The salary range posted reflects our target baseline for this role. Final compensation is determined by a thorough evaluation of factors including the candidate’s specific experience, localized market data, and internal team equity.