Senior Forward Deployed Engineer
At Shakudo, we’re building the world’s first operating system for data and AI. We use the term “operating system” in the truest sense: just like iOS, Windows, or Linux, Shakudo’s end-to-end OS provides ever-evolving, fully automated, best-in-class open-source components tailored to each business’s unique needs.
We are seeking a Senior Forward Deployed Engineer to join our ProServe team and work directly with strategic customers to turn business problems into production AI and data systems. This is a highly technical, customer-facing role for someone who can move between customer conversations, system design, data engineering, AI application development, Kubernetes, cloud infrastructure, and production support.
In this role, you will own the path from discovery to production: understanding the customer’s workflows and data, designing the solution, building and deploying it on Shakudo, and helping the customer adopt it in real operations. This is an outcome-based engineering role. A successful engagement is not a demo, a prototype, or an installation. It is a live, governed, adopted workflow that improves how the customer operates and is tied to a clear business result.
Responsibilities
Embed with customer teams to understand business problems, workflows, data systems, constraints, and success metrics.
Define what success looks like for each engagement, including the target outcome, adoption path, production boundary, and measurable impact.
Translate ambiguous customer needs into clear technical scopes, architectures, implementation plans, and production outcomes.
Build and deploy AI and data applications on Shakudo, including agentic workflows, RAG systems, data pipelines, integrations, evaluations, and operational automation.
Design and implement production data workflows across enterprise environments, including ingestion, transformation, orchestration, data quality, access control, and observability.
Deploy and operate Shakudo in complex customer environments, including cloud, hybrid, on-prem, private cloud, and air-gapped infrastructure.
Work hands-on with Kubernetes, containers, networking, storage, identity, secrets, observability, and production troubleshooting.
Partner with customer engineering, platform, data, and security teams to get systems live, governed, adopted, and measurable.
Participate in PagerDuty-based production support for customer deployments, including incident response, escalation, root-cause analysis, and follow-up remediation.
Turn customer-specific work into reusable patterns, playbooks, templates, and product feedback for Shakudo.
Qualifications
8+ years of experience across software, data, platform, infrastructure, or AI engineering roles, including:
3+ years building LLM/AI applications such as RAG, agents, evaluations, workflow automation, or production AI systems.
5+ years working with Kubernetes and cloud-native infrastructure in production environments.
Strong experience with major cloud platforms such as AWS, Azure, or GCP.
Strong data engineering background, including pipelines, orchestration, transformation, data quality, access controls, and production data workflows.
Experience with a modern data stack such as Spark, Airflow, Databricks, Snowflake, or similar.
Experience building or deploying AI, data, or automation solutions in highly regulated or operationally complex industries, such as financial services, government, healthcare, energy, agriculture, supply chain, or industrial operations.
Ability to apply AI to real-world operational data, such as sensor data, geospatial data, logistics data, ERP data, field operations data, or forecasting data.
Proficiency in at least one production programming language such as Python, Go, TypeScript, Java, or Scala.
Strong systems thinking across data, users, permissions, workflows, infrastructure, governance, and business processes.
Excellent customer-facing communication skills with engineers, operators, security teams, executives, and business owners.
Strong ownership mindset: you care about production rollout, adoption, reliability, operational handoff, and measurable impact.
Willingness to travel to customer sites as needed.
A Plus
Experience in a forward-deployed, professional services, solutions architecture, customer engineering, field engineering, or technical consulting role.
Experience delivering outcome-based customer engagements where success was measured by adoption, operational improvement, or business impact.
Experience with data engineering, machine learning, or data science workflows, including feature engineering, model training, experimentation, evaluation, or production ML systems.
Experience with on-prem, private cloud, regulated, hybrid, or air-gapped deployments.
Experience with infrastructure-as-code and production operations.
Experience working with enterprise security, compliance, audit, access control, and governance requirements.
Experience integrating AI or data systems with enterprise applications, internal APIs, data platforms, or customer-specific operational tools.
Experience leading senior technical stakeholders through architecture reviews, security reviews, implementation planning, and production-readiness decisions.
Ability to identify repeatable product and service opportunities from customer-specific implementations.