Forward Deployed Engineer, Life Sciences
New
J
JobgetherLife Sciences, AI
United StatesFull-TimeMiddle
Salary not disclosed
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Job Details
- Required Skills
- AWSDockerPythonSQLBashGCPKubernetesAzureRMLOps
Requirements
- Strong software engineering background with deep proficiency in Python.
- Working familiarity with SQL, R, and Bash.
- Experience with Kubernetes and managed services such as EKS, AKS, or GKE.
- Hands-on experience with Docker and cloud architecture across AWS, Azure, and/or GCP.
- Ability to troubleshoot networking, compute, infrastructure, and platform-level issues.
- Demonstrated experience delivering machine learning workflows, including model deployment and monitoring.
- Experience with GPU workloads and generative AI or agent frameworks.
- Experience working in or delivering technology within highly regulated or constrained environments.
- Ability to navigate requirements involving compliance, data security, and infrastructure limitations.
- Strong consultative communication skills for technical and business stakeholders.
- Experience leading discovery sessions and technical solutioning discussions.
- Ability to become productive quickly within unfamiliar codebases and architectures.
Responsibilities
- Learn the platform, customer environment, technical architecture, data landscape, tooling, and business objectives during onboarding.
- Progress toward full ownership of strategic life sciences customer engagements, independently managing prioritized technical backlogs.
- Design, build, test, and deploy production-grade AI and machine learning solutions within customer environments.
- Deliver solutions across the MLOps lifecycle, including development, deployment, monitoring, and operationalization of models and applications.
- Build specialized AI inference workflows, custom cloud and data integrations, and interactive applications.
- Advise customer data science and engineering teams on platform practices, architecture, and MLOps workflows.
- Conduct structured discovery conversations and translate complex or ambiguous customer requirements into practical technical solutions.
- Troubleshoot infrastructure, networking, compute, Kubernetes, and cloud issues in constrained environments.
- Serve as a trusted technical advisor for technical and business stakeholders.
- Create reusable playbooks, integration templates, and deployment guides to improve delivery efficiency.
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