Senior Software Engineer - Platform AI Infrastructure

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Picture a company redefining how life sciences harness data — one that turns the noise of fragmented scientific systems into the clarity that accelerates discovery, development, and ultimately, human progress.

This Scientific Data Cloud pioneer has built a cloud-native ecosystem engineered specifically for the life sciences, connecting laboratory instruments, informatics systems, and analytics applications into a single, intelligent network. The result: harmonized, actionable scientific data that transforms R&D velocity and precision across discovery, development, and manufacturing.

Trusted by the world’s leading biopharma innovators, their open platform serves as the digital nervous system for scientific operations — empowering researchers and partners to unlock insights at unprecedented scale.

Think of it as the Palantir of Life Sciences — designed not just to visualize complexity, but to ingest and process petabytes of scientific data through advanced taxonomies and ontologies that bring structure, context, and meaning to an otherwise chaotic scientific landscape.

Through deep collaborations with global leaders in cloud computing and AI, this company is building the foundation for a new era of Scientific Intelligence — one where every experiment, every dataset, and every discovery is connected, contextualized, and exponentially more powerful than before.

Senior Platform Engineer — Distributed Systems & AI Infrastructure

We’re looking for experienced Senior Platform Engineers with deep expertise in distributed systems, high availability, and large-scale data engineering in cloud-native environments.

Your mission: build a next-generation AI platform that empowers scientists and engineers to operationalize advanced machine learning models at global scale.

This company operates at the intersection of life sciences, cloud computing, and AI, transforming how scientific data is collected, harmonized, and activated. Their cloud-native platform unifies data from complex laboratory and informatics systems into a single, intelligent framework — unlocking faster, more reproducible breakthroughs in discovery and development.

Trusted by the world’s leading biopharma innovators, this organization is shaping the future of Scientific Intelligence, enabling researchers to leverage petabyte-scale data pipelines, advanced ontologies, and AI-driven analytics to accelerate progress from lab to life.

What You’ll Do

As a Senior Platform Engineer, you’ll help architect and build our client’s proprietary, next-generation AI platform — their own internal equivalent to AWS SageMaker. This platform will serve as the foundation for developing, training, and deploying advanced AI models across global scientific and biopharma environments.

Much like SageMaker, which provides a fully managed environment for machine learning, this system will enable teams to:

• Rapidly build, train, and deploy AI models at scale,

• Seamlessly integrate data pipelines for high-volume ingestion and transformation, and

• Deliver secure, reliable, and production-grade AI workflows across distributed cloud infrastructure.

You’ll collaborate across data, AI, and engineering teams to design resilient systems that power the company’s most ambitious machine learning and scientific data initiatives — enabling automation, scalability, and operational excellence at the intersection of AI and life sciences.

Responsibilities

• Architect, build, and maintain cloud-native infrastructure for AI and data workloads using platforms like Databricks and AWS Bedrock.

• Develop scalable data pipelines to ingest, transform, and serve data for ML, analytics, and scientific applications.

• Implement infrastructure-as-code using tools such as CloudFormation and AWS CDK to ensure consistency and security.

• Partner with AI engineers and data scientists to optimize model deployment, monitoring, and performance.

• Lead observability best practices, including advanced monitoring, alerting, and logging across AI systems.

• Evolve the AI platform to support emerging frameworks, data modalities, and use cases.

• Research and recommend cutting-edge tools and approaches to improve scalability, cost-efficiency, and speed.

• Integrate AI and LLM-based architectures (e.g., retrieval-augmented generation) into production environments.

What You Bring

Preferred Experience

• Familiarity with emerging LLM orchestration frameworks (e.g., DSPy) for complex prompt pipelines.

• Experience with vector databases / embedding stores (e.g., OpenSearch, Pinecone) for semantic search and retrieval.

• Understanding of LLM cost optimization, latency reduction, and usage analytics at scale.

Required Experience

• 7+ years of professional experience in software or infrastructure engineering, including production AI systems.

• Proven expertise in building and maintaining ML infrastructure, including model deployment, lifecycle management, and automation.

• Deep knowledge of AWS and modern infrastructure-as-code frameworks (ideally CDK).

• Expert-level proficiency in TypeScript and Python for backend and API development.

• Hands-on experience with Databricks MLFlow, including model registration, versioning, and serving.

• Strong understanding of containerization (Docker), CI/CD pipelines, and orchestration tools (e.g., ECS).

• Demonstrated ability to design secure, scalable, and fault-tolerant infrastructure for real-time and batch AI workloads.

• Excellent communication skills and the ability to collaborate effectively with cross-functional teams.