Sr. Forward Deployed Engineer - Private Cloud, Data & AI Enterprise Solutions
Remote (US)Full-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 10+ years
- Required Skills
- DockerNode.jsPythonSQLETLKubernetesVue.JsGoNosqlReactCI/CDPrompt EngineeringLangChain
Requirements
- BS/MS/PhD in Computer Science, Data Science, Engineering, Mathematics, Physics, or related field.
- 10+ years in software engineering, data engineering, or AI/ML delivery.
- At least 4+ years in customer-facing or field roles.
- Proven track record in building and deploying AI/ML applications in production at enterprise scale.
- Deep full-stack proficiency: Python (required), Node.js/Go, React/Vue, SQL/NoSQL databases.
- Hands-on with LLMs, prompt engineering, vector databases, data pipelines, application dashboards, RAG pipelines, and agent orchestration frameworks.
- Strong DevOps skills: Docker, Kubernetes, CI/CD, GPU infrastructure, cloud-native deployment patterns.
- Experience integrating across heterogeneous enterprise systems - ERP, data warehouses, data lakes, streaming architectures.
- Ability to translate ambiguous customer needs into actionable engineering plans under tight timelines.
- Excellent communication skills - comfortable with C-suite presentations, technical workshops, and cross-functional collaboration.
- Willingness to travel up to 25% for on-site customer engagements.
Responsibilities
- Embed with strategic enterprise customers to rapidly diagnose critical business challenges, map data landscapes, and co-design AI solutions on-site.
- Lead end-to-end solution design and delivery of agentic AI workflows, RAG pipelines, knowledge graphs, and real-time decision-making applications.
- Drive rapid prototyping and POCs that demonstrate tangible business value within days to weeks.
- Serve as the primary technical owner across the full project lifecycle: scoping, architecture, build, deployment, and post-launch optimization.
- Architect production-grade Enterprise AI applications on Partner Foundry Solutions or Rackspace Private Cloud and GPU infrastructure, integrating with enterprise systems.
- Build scalable data pipelines across structured and unstructured data using ETL/ELT, vector databases, and knowledge base frameworks.
- Develop and fine-tune LLM/SLM solutions; implement RAG architectures and orchestrate multi-agent workflows.
- Ship with full-stack and DevOps depth: Python, Node.js/Go, React/Vue, Docker, Kubernetes, CI/CD, and GPU cluster management.
- Champion observability, monitoring, and telemetry to ensure trustworthy, auditable, and versioned AI agents in production.
- Identify expansion opportunities by working with sales and customer success to uncover high-value use cases across new business domains.
- Feed structured field insights back to Platform Engineering and Product on feature gaps, emerging needs, and usability improvements.
- Build reusable IP through reference architectures, accelerators, frameworks, and technical best practices that scale future engagements.
- Mentor engineers and customer teams, driving knowledge transfer and building internal AI competencies.
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