Forward Deployed AI Engineer (Post-Sales)
Lead post-sales deployment and adoption of DatologyAI's data curation platform for enterprise customers. Act as technical owner for on-prem and hybrid AI/ML deployments across AWS, GCP, Azure, and Kubernetes.
About the job
What You'll Work On
- Lead customers through onboarding, deployment, and production rollout of DatologyAI's platform while serving as the technical owner for assigned accounts—driving architecture, execution, long-term adoption, and tailored technical success plans.
- Partner cross-functionally with Sales, Engineering, and Research to translate use-case requirements into actionable technical strategies, support early trials, relay customer feedback, and help shape roadmap priorities.
- Guide customers in designing scalable, secure workflows across compute, storage, networking, and distributed systems, providing ongoing reporting on deployment progress, workload health, usage metrics, and executive-level updates.
- Adapt and optimize DatologyAI's platform across AWS, GCP, Azure, and on-prem Kubernetes environments, handling provider-specific APIs, storage systems, networking configurations, and compute orchestration—including tuning performance for network topology, storage tiering, and resource allocation in each environment.
About You
- 5+ years of experience in technical roles involving solution architecture, customer engineering, consulting, or technical program delivery.
- Strong background in distributed systems, data infrastructure, and/or on-prem or hybrid compute environments.
- Experience working with ML/AI workflows, designing or deploying systems involving Kubernetes, networking, data pipelines, or large-scale backend infrastructure.
- Proficiency in Python, SQL, or similar languages, with the ability to contribute to technical conversations and debug customer issues end-to-end.
- Experience leading complex technical projects with multiple stakeholders—translating business needs into clear architecture and execution plans.
- Deep hands-on experience with multiple cloud platforms (AWS, GCP, Azure) including their compute, storage, networking, and IAM services.
- Proven track record of adapting complex distributed systems to run across different infrastructure environments.
- Expertise in infrastructure-as-code and configuration management for multi-environment deployments.
- Required to travel to customer sites as needed to support critical deployments and customer engagements.
Compensation & Benefits
- Salary range: $230,000 to $300,000.
- 100% covered health benefits (medical, vision, and dental).
- 401(k) plan with a generous 4% company match.
- Unlimited PTO policy.
- Annual $2,000 wellness stipend.
- Annual $1,000 learning and development stipend.
- Daily lunches and snacks provided in office.
- Relocation assistance for employees moving to the Bay Area.
Skills
Python, SQL, Kubernetes, AWS, GCP, Azure, Distributed Systems, Infrastructure As Code, Ml/Ai Workflows, Data Pipelines
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