Senior Forward Deployed Engineer

Jobtailor

San Antonio (TX)

On-site

USD 180,000 - 280,000

Full time

14 days+

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Job summary

Jobtailor in San Antonio seeks a senior Enterprise AI Architect to diagnose business challenges, map data landscapes, and co-design AI solutions on-site. You will lead end-to-end solution design and delivery of agentic AI workflows, RAG pipelines, knowledge graphs, and real-time decision-making applications.

You will prototype rapidly, own the full lifecycle, architect production-grade AI apps across enterprise platforms such as Palantir Foundry, and build scalable data pipelines.

Qualifications

Bachelor's degree in computer science, engineering, or related technical discipline.
Must be Palantir certified.
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, Node.js/Go, React/Vue.
Hands-on with LLMs, vector databases, data pipelines, RAG pipelines, and agent orchestration frameworks.

Responsibilities

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 AI agents in production
Identify expansion opportunities by working with sales and customer success to uncover high-value use cases across new domains
Feed structured field insights back to Platform Engineering and Product on feature gaps and usability improvements
Build reusable IP through reference architectures, accelerators, frameworks, and best practices
Mentor engineers and customer teams, driving knowledge transfer and building internal AI competencies

Skills

Leadership in AI projects

Excellent communication

Customer-facing experience

Cross-functional collaboration

Architect enterprise AI systems

Education

Bachelor's degree in computer science or engineering

Tools

Palantir Foundry

Docker

Kubernetes

GPU infrastructure

OpenStack

VMware

LangChain

LangGraph

Job description

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

Bachelor’s degree in computer science, engineering, or related technical discipline required
Must be Palantir certified
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
Experience with Palantir Foundry, AIP, ontology modeling, Uniphore BAIC, or similar Enterprise AI development platforms
Knowledge of SLM fine-tuning, model distillation, RLHF, and AI evaluation frameworks
Experience building agentic AI solutions: multi-agent systems, tool use, and autonomous workflow orchestration
Familiarity with GPU infrastructure (NVIDIA H100/B200, InfiniBand) and private cloud platforms (OpenStack, VMware)
Prior experience in technology consulting, AI startups, or Forward Deployed / Solutions Engineering roles
Domain expertise in financial services, healthcare, supply chain, defense, energy, or manufacturing
Experience with knowledge graphs, semantic modeling, and ontology-driven data management.

Core Competencies

Demonstrates expertise in architecting and delivering enterprise-scale AI applications, leveraging deep full-stack proficiency in Python, Node.js/Go, and React/Vue. Proven ability to integrate complex data landscapes and drive customer-facing AI/ML solutions with strong DevOps practices and effective communication skills.

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