Forward Deployed Engineer (Life Sciences)

Join Domino, a leading AI business solutions platform, as a Forward Deployed Engineer in the Life Sciences vertical. You will be embedded in a customer's environment, building AI workflows and deploying production-grade solutions. Your impact will unfold across key milestones, from onboarding and shadowing to full customer ownership and strategic scale. Enjoy benefits such as premium medical, dental, and vision insurance, commuter benefits, flexible paid time off, and annual education reimbursement.

Assumer la responsabilité de la gestion complète des comptes clients stratégiques dans le secteur des sciences de la vie, en exécutant de manière autonome un backlog priorisé.

Construire et déployer des solutions de production à grande échelle tout au long du cycle de vie MLOps, tout en conseillant activement les équipes de science des données sur les meilleures pratiques.

We are looking for a sharp, action-first engineer who possesses a unique blend of deep technical grit and customer empathy

Responsibilities

• Agir en tant que conseiller de confiance pour les parties prenantes techniques et commerciales, en rédigeant des guides de déploiement et des modèles d'intégration réutilisables.

Requirements

• Consultative Mindset: Exceptional communication skills across both technical and business audiences. You are comfortable running structured discovery conversations, leading technical solutioning sessions, and translating messy customer requirements into buildable, tested solutions
• Execution Style: An "act fast, own it" mentality. You thrive in ambiguity, do not let blockers sit, and are comfortable making yourself productive inside an unfamiliar codebase quickly
• Core Technical Stack: Strong engineering roots with deep proficiency in Python (primary), alongside familiarity with SQL, R, and Bash
• Regulated Environment Navigation: Experience operating within, or consulting for, highly regulated or constrained environments. You can expertly navigate multi-factor constraints like strict compliance rules, data security hurdles, and infrastructure limits
• AI & MLOps Experience: A proven track record of delivering machine learning workflows, model deployment/monitoring, GPU workloads, and generative AI or agent frameworks
• Cloud & Infrastructure: Experience with Kubernetes and managed K8s solutions (such as EKS, AKS, or GKE), Docker, and cloud architecture (AWS, Azure, or GCP). You should be capable of troubleshooting networking, compute, and platform issues