Forward Deployed Engineer, Life Sciences

engineeringjobs.net, Inc.; Town of Montana (WI); Remote; USD 120,000 - 180,000; Full time

Benefits offered by this job

Remote work within United States

Job summary

Jobgether seeks a seasoned AI/ML Solutions Architect to onboard clients, design production-grade AI solutions, and drive MLOps implementations. You will collaborate with engineering and data science teams to translate complex requirements into scalable deployments across cloud environments.

The role emphasizes deep technical leadership, ownership of strategic engagements, and delivering compliant, production-ready workflows in highly regulated life sciences settings.

Qualifications

• Strong software engineering background with deep proficiency in Python and familiarity with SQL, R, and Bash.
• Experience with Kubernetes and managed Kubernetes 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 in complex environments.
• Demonstrated experience delivering machine learning workflows, including model deployment and monitoring.

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 backlogs.

Skills

Python

SQL

Bash

Kubernetes

Docker

Cloud (AWS/Azure/GCP)

Generative AI

AI/ML deployment

Monitoring

Tools

EKS

AKS

GKE

Job description

Accountabilities

• Learn the platform, customer environment, technical architecture, data landscape, tooling, and business objectives during onboarding, working closely with experienced engineering colleagues.
• Progress toward full ownership of strategic life sciences customer engagements, independently managing prioritized technical backlogs in partnership with Engagement Managers.
• 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, interactive applications, and solutions supporting areas such as Statistical Computing Environments and clinical CRM.
• Advise customer data science and engineering teams on effective platform practices, architecture, deployment approaches, and MLOps workflows.
• Conduct structured discovery conversations and translate complex or ambiguous customer requirements into practical, testable technical solutions.
• Troubleshoot infrastructure, networking, compute, Kubernetes, cloud, and platform issues in constrained and highly regulated environments.
• Develop strong relationships with technical and business stakeholders and serve as a trusted technical advisor within strategic accounts.
• Create reusable playbooks, integration templates, deployment guides, and other resources that improve delivery efficiency across the broader engineering practice.
• Capture customer and field intelligence and communicate relevant platform opportunities, issues, and requirements to SRE, Support, and Product teams.
• Contribute to continuous improvement of delivery practices, technical approaches, and reusable solutions across life sciences engagements.

Requirements

• Strong software engineering background with deep proficiency in Python and working familiarity with SQL, R, and Bash.
• Experience with Kubernetes and managed Kubernetes 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 in complex environments.
• Demonstrated experience delivering machine learning workflows, including model deployment and monitoring.
• Experience with GPU workloads and generative AI, agent frameworks, or related AI technologies.Experience working in, consulting for, or delivering technology within highly regulated or otherwise constrained environments.
• Ability to navigate requirements involving compliance, data security, infrastructure limitations, and other operational constraints.
• Strong consultative communication skills, with the ability to engage effectively with both technical and business stakeholders.
• Experience leading discovery sessions, technical solutioning discussions, and translating complex requirements into buildable solutions.
• Action-oriented approach with strong ownership, initiative, and comfort working through ambiguity.
• Ability to become productive quickly within unfamiliar codebases, technical environments, and customer architectures.
• Strong customer empathy combined with technical depth and practical problem-solving skills.
• Growth mindset, intellectual curiosity, and commitment to continuous learning and improvement.
• Ability to collaborate effectively while working independently in fast-moving customer environments.

Benefits

• Remote work opportunity within the United States.
• Opportunity to work directly with major life sciences and pharmaceutical organizations on high-impact AI initiatives.
• Hands-on exposure to production AI, machine learning, MLOps, cloud infrastructure, and emerging agent-based technologies.
• Opportunity to solve complex technical challenges within highly regulated environments.
• Direct customer engagement and the chance to develop trusted-advisor relationships with technical and business leaders.
• Significant ownership and autonomy across strategic customer engagements.
• Opportunities to create reusable technical assets and influence platform and product development through field insights.
• Learning-focused environment with an emphasis on teaching, knowledge sharing, and professional growth.
• Collaborative and inclusive workplace that values diverse backgrounds, perspectives, and experiences.
• Startup-oriented environment offering the opportunity to contribute to evolving technology, processes, and engineering practices.