Lead Software Platform Engineer, MLOps

🕒 Agosto 4

🏄 California, Massachusetts – Remoto

🏄 California – Remote 🍂 Massachusetts – Remote

💵 $200.000 - $270.000 / ano

⏰ Tempo Integral

🟠 Sênior

🏗️ Engenheiro de Plataforma

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TetraScience

SiteLinkedInTodas as Vagas

51 - 200 funcionários

Fundada em 2015

🏥 Saúde

💼 Consultoria

📦 Logística

Healthcare • Consulting • Logistics

A TetraScience é uma empresa dedicada a transformar dados científicos brutos em conjuntos de dados nativos de IA para aplicações científicas avançadas. Ao colaborar estreitamente com as principais empresas biofarmacêuticas, a TetraScience aumenta a produtividade, acelera insights e garante a integridade dos dados em toda a cadeia de valor científica. Sua plataforma oferece soluções para gerenciamento de dados de laboratório de próxima geração, resultados científicos impulsionados por IA e conformidade com os padrões da indústria. Como a primeira empresa a fornecer um cloud de dados e IA construído especificamente para a ciência, a TetraScience permite que seus clientes liberem, unifiquem e transformem seus dados, superando silos de dados tradicionais e impulsionando a produtividade científica ao oferecer uma infraestrutura flexível, aberta e colaborativa.

Descrição

• Own the technical architecture of the AI/ML platform and its customer-facing service and API surface. • Own the end-to-end model and prompt lifecycle across Databricks MLflow and AWS Bedrock, including registration, versioning, staged promotion, rollback, and multi-model serving. • Design inference infrastructure for real-time and batch AI workloads, including routing, batching, caching, concurrency control, GPU/accelerator capacity planning, large binary inputs, and graceful degradation. • Integrate AI/LLM systems using RAG and architect agentic layers with tool/function calling, MCP tooling, and agent runtimes. • Design platform security, including guardrails, prompt-injection and tool-abuse defenses, PII/PHI handling, and tenant data boundaries. • Build AI evaluation and quality infrastructure, including eval harnesses, golden datasets, CI regression gates, A/B and shadow deployment, and drift/hallucination detection. • Establish monitoring, alerting, logging, distributed tracing, and SLI/SLO/SLA practices for the AI platform. • Design reproducibility and lineage for validated pharmaceutical environments, including versioned data, code, prompts, model artifacts, and audit trails. • Contribute to infrastructure-as-code and deployment automation using CloudFormation and AWS CDK. • Own production readiness, performance, reliability, cost efficiency, incident response, and runbooks with Applied AI, data engineering, and platform teams. • Act as subject-matter expert and design authority; lead design reviews, write reference architectures and documentation, and mentor engineers. • Evaluate emerging AI infrastructure frameworks, serving runtimes, model providers, and data types, making build-versus-buy decisions.

🎯 Requisitos

• 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. • Demonstrated experience as a technical leader or architect, accountable for key decisions on system design, scalability, performance, and cost optimization. • Experience designing security into a multi-tenant platform, including authorization boundaries between tenants and handling sensitive data such as PII and PHI, with awareness of LLM-specific risks like prompt injection and tool abuse. • Extensive experience building and maintaining AI/ML infrastructure in production as a multi-tenant product with external users. • 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. • Expert-level coding skills in TypeScript and Python building 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, including eval harnesses, regression gates, and drift or quality monitoring. • Proficiency in API-first design, including REST and OpenAPI specifications. • Solid working knowledge of AWS and containerized workloads such as Docker. • Familiarity with infrastructure-as-code such as CloudFormation or CDK, CI/CD pipelines, and deployment automation. • Experience defining observability and SLI/SLO/SLA practice, including monitoring, alerting, and distributed tracing. • Ability to articulate ideas clearly to customers and cross-functional teams, influence technical direction, and mentor engineers. • Nice to have: familiarity with emerging LLM frameworks, production agentic orchestration, MCP, LLM cost monitoring, multimodal model inputs, fine-tuning or model optimization, regulated/validated environments, and scientific, life sciences, or laboratory data domains. • Visa sponsorship is not currently provided for this position.

🏖️ Benefícios

• 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 where you can grow your career and get coaching

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