Senior Forward Deployed Engineer
New
J
JobgetherAI Software
Fully remote work-from-home arrangement within the United States.Full-TimeSenior
Salary$207,957 to $305,003.60, depending on geographic market, job-related knowledge, skills, and experience.
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Job Details
- Experience
- 10+ years of experience in software engineering, data engineering, or AI/ML delivery, including at least 4 years in customer-facing, consulting, or field engineering roles.
- Required Skills
- Node.jsPythonSQLKubernetesVue.JsGoReact
Requirements
- Palantir certification is required.
- 10+ years of experience in software engineering, data engineering, or AI/ML delivery.
- At least 4 years of experience in customer-facing, consulting, or field engineering roles.
- Demonstrated success building and deploying enterprise-scale AI/ML applications into production.
- Deep full-stack engineering capabilities including Python and experience with Node.js or Go, React or Vue, and SQL/NoSQL databases.
- Hands-on experience with LLMs, prompt engineering, vector databases, RAG architectures, and agent orchestration frameworks.
- Strong DevOps expertise with Docker, Kubernetes, CI/CD, GPU infrastructure, and cloud-native deployment practices.
- Experience integrating enterprise systems including ERP platforms, data warehouses, and data lakes.
- Experience with enterprise AI platforms such as Palantir Foundry and AIP, or comparable technologies.
- Demonstrated experience building agentic AI solutions involving multi-agent systems and autonomous workflow orchestration.
- Ability to travel up to 25% for customer engagements.
Responsibilities
- Diagnose complex customer business challenges, assess data landscapes, and collaboratively define high-value AI opportunities and solution approaches.
- Lead the end-to-end design and delivery of agentic AI workflows, RAG pipelines, knowledge graphs, and real-time decision-making applications.
- Develop rapid prototypes and proof-of-concepts that demonstrate measurable business value.
- Serve as the primary technical owner throughout the solution lifecycle, including discovery, scoping, architecture, development, deployment, and post-launch optimization.
- Architect and deploy production-grade enterprise AI applications across private cloud, AI platforms, and GPU infrastructure.
- Build scalable data pipelines supporting structured and unstructured data using ETL/ELT processes, vector databases, and knowledge-base frameworks.
- Establish observability, monitoring, telemetry, versioning, and auditability practices to support trustworthy AI applications in production.
- Build reusable intellectual property, including reference architectures, accelerators, and technical standards.
- Mentor engineers and customer teams through knowledge transfer and technical guidance.
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