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 (ERP, CRM, data warehouses, data lakes)
Build scalable data pipelines across structured and unstructured data using ETL/ELT, vector databases (Pinecone, Weaviate, AstraDB), and knowledge base frameworks
Develop and fine-tune LLM/SLM solutions; implement RAG architectures (LlamaIndex, Haystack) and orchestrate multi-agent workflows (LangChain, LangGraph, CrewAI)
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.
Requirements
BS/MS/PhD in Computer Science, Data Science, Engineering, Mathematics, Physics, or related field
3+ years in software engineering, data engineering, or AI/ML delivery; 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