Lead Software Platform Engineer, MLOps

🕒 il y a 2 mois

🏄 California, Massachusetts – Distant

🏄 California – Remote 🍂 Massachusetts – Remote

💵 $200 000 - $270 000 / an

⏰ Temps Plein

🟠 Senior

🏗️ Ingénieur Plateforme

🦅 Parrain de Visa H1B

Cette entreprise a parrainé des visas H1B par le passé.

👻 Score fantôme 18%

Ce que ça veut dire : la probabilité que personne ne recrute vraiment pour cette offre.Comment le lire : 60 % ou plus, on pense que c'est une offre fantôme. Un score plus bas est moins inquiétant.Comment on la calcule : ancienneté, republications, postes ouverts vs taille de l'équipe, rapidité de clôture, et si le salaire est indiqué.À noter : une estimation à partir de données publiques, pas un jugement sur l'entreprise. De vraies offres peuvent avoir un score élevé, alors prenez-le comme un indice, pas comme un fait.

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TetraScience

Site WebLinkedInTous les Emplois

51 - 200 employés

Fondée en 2015

🏥 Santé

💼 Conseil

📦 Logistique

Healthcare • Consulting • Logistics

TetraScience est une entreprise dédiée à la transformation des données scientifiques brutes en ensembles de données natifs pour l'IA, destinés à des applications scientifiques avancées. En collaborant étroitement avec les principales entreprises biopharmaceutiques, TetraScience améliore la productivité, accélère les insights et assure l'intégrité des données tout au long de la chaîne de valeur scientifique. Leur plateforme propose des solutions pour la gestion des données de laboratoire de nouvelle génération, des résultats scientifiques pilotés par l'IA et la conformité avec les normes industrielles. En tant que première entreprise à fournir un cloud de données et d'IA conçu spécifiquement pour la science, TetraScience permet à ses clients de libérer, unifier et transformer leurs données, surmontant les silos de données traditionnels et boostant la productivité scientifique grâce à une infrastructure flexible, ouverte et collaborative.

Description

• 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.

🎯 Exigences

• 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.

🏖️ Avantages

• 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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