AI Implementation Engineer

• Design and Build AI Solutions for On-Prem systems in Air-Gapped environment:
  Design and implement end-to-end agentic AI systems that support planning, reasoning, tool use, and multi-step execution in real-world environments. Build modular, testable components that move from prototype to operational capability.
• Integrate Models and Tools for On-Prem systems in Air-Gapped environment:
  Develop integrations across LLMs, APIs, data sources, and Model Context Protocol (MCP) interfaces to enable intelligent agents to interact with external systems, retrieve context, and take action safely and reliably.
• Develop Retrieval Pipelines for On-Prem systems in Air-Gapped environment:
  Build and optimize Retrieval-Augmented Generation (RAG) pipelines that connect models to live knowledge sources, structured data, and enterprise content to improve factual grounding, contextual relevance, and response quality.
• Engineer Conversational and Agentic Interfaces for On-Prem systems in Air-Gapped environment:
  Create conversational systems and intelligent agents with memory, contextual awareness, adaptive decision-making, and support for multi-turn user and system interactions.
• Implement and Evaluate AI Workflows for On-Prem systems in Air-Gapped environment:
  Translate technical objectives into working pipelines, run experiments, evaluate agent behavior, and iterate on prompts, orchestration logic, retrieval quality, and system performance to improve reliability and usability.
• Architect local infrastructure to size, config, and optimize local CPU/GPU workloads, utilizing quantization techniques to maximize throughput, etc.
• Orchestrate disconnected environments, design and maintain offline model update pipelines, local package mirrors, etc.
• Scope and Define Requirements:
  Gather, document, and validate technical and functional requirements from project artifacts, stakeholders, and mission needs to ensure feasibility, completeness, and alignment with operational goals.
• Collaborate Across Teams:
  Work closely with engineers, technical leads, and mission stakeholders to integrate AI capabilities into broader software and system architectures. Participate in technical reviews, design discussions, and delivery planning.
• Support Technical Quality:
  Contribute to testing, debugging, and performance optimization of AI-enabled applications, including edge cases involving context management, retrieval failures, tool execution, and orchestration logic.
• Learn and Apply Emerging Practices:
  Stay current on advances in LLMs, agent frameworks, orchestration methods, and applied AI engineering practices, and bring that knowledge into practical system design and implementation.
• Communicate Technical Work:
  Clearly document architectures, workflows, assumptions, and implementation decisions so that solutions are maintainable, explainable, and transferable across teams.

• Bachelor’s Degree in Information Technology, Computer Science, Computer Engineering, Electrical Engineering, Systems Engineering, Physics, Math, or equivalent full-time professional experience;
  Master’s Degree in Engineering or other technical field highly desired
• 5-10+ years of professional experience in software engineering, machine learning engineering, AI engineering, or related technical roles
• Proficiency in Python, including experience with core libraries such as Num Py and Pandas
• Deep hands-on experience with production local inference engines such as vLLM, SGLang, Triton Inference Server, or TensorRT-LLM
• Experience building software with one or more modern AI/ML frameworks such as PyTorch, Tensor Flow, Lang Chain, Lang Graph, Semantic Kernel, or Auto Gen
• Experience with Linux and hardening (fapolicy/selinux/fips/etc)
• Experience with commercially available AI tooling/chat
• Experience with hosting LLM servers
• Experience with hosting different models (chat/embedding)
• Experience with distributed networking (reverse proxy/load balancing/firewalls/etc)
• Experience with containerization
• Ability to work independently on technical tasks while collaborating effectively in a team environment
• Ability to shift from one project to another in an agile work environment
• Strong leadership capabilities and skills
• Strong documentation skills
• Ability to travel up to 10% of the time, as needed

Demonstrates expertise in designing and implementing AI solutions in air-gapped environments, with a strong focus on building modular components, integrating models, and optimizing retrieval pipelines. Proficient in Python and experienced with modern AI/ML frameworks, local inference engines, and containerization technologies.

• Python Proficiency
• AI/ML Framework Experience
• Local Inference Engine Expertise
• Containerization Experience
• Strong Leadership Skills

• Python
• Num Py
• Pandas
• VLLM
• SGLang
• Triton Inference Server
• TensorRT-LLM
• Py Torch
• Tensor Flow
• Lang Chain

• Strong Leadership
• Strong Documentation Skills
• Ability to Collaborate Effectively
• Ability to Work…