Applied AI Engineer - Public Sector

webAI•Austin, TX

55d

About The Position

We are seeking an AI Systems Engineer to support our Public Sector initiatives focused on building and optimizing production ready AI systems for secure and distributed environments. This role sits at the intersection of machine learning, systems engineering, and deployment optimization, bridging research and real world implementation. You will be responsible for transforming prototype models into scalable, efficient, and reliable production systems that operate seamlessly across a spectrum of hardware from government cloud infrastructure to edge devices in restricted or disconnected environments.

Requirements

Active US Security clearance or eligibility and willingness to obtain a US Security clearance
5+ years of experience in applied AI, ML engineering, or production AI systems.
Deep proficiency in PyTorch, TensorFlow, or Hugging Face Transformers.
Proven experience deploying AI models across cloud, edge, and mobile hardware environments.
Expertise in model compression and optimization (quantization, pruning, distillation).
Strong understanding of GPU computing, CUDA, and performance profiling.
Experience building RAG pipelines and integrating vector databases (e.g., FAISS, Milvus, Pinecone).
Familiarity with multi modal models and synthetic data generation methods.
Low level programming experience in C, C++, or Rust with understanding of computer memory and concurrency.
Strong algorithmic and problem solving skills, especially in distributed or constrained compute environments.

Nice To Haves

Experience with edge AI, federated learning, or offline inference systems.
Familiarity with distributed training frameworks such as DeepSpeed or Ray.
Understanding of AI governance and compliance frameworks relevant to public sector deployments.
Experience integrating models into large scale distributed systems or microservice architectures.
Excellent communication and technical documentation skills for collaboration across multi disciplinary teams.

Responsibilities

Productionize AI models from research prototypes into scalable, deployable systems used in real world applications.
Develop, fine tune, and optimize models using PyTorch, TensorFlow, or Hugging Face Transformers, adapting both open and closed source models.
Implement model optimization techniques such as quantization, pruning, distillation, and hardware specific acceleration.
Engineer systems for dynamic model adaptation using low rank adaptation (LoRA), parameter efficient fine tuning (PEFT), and on device inference strategies.
Build and maintain Retrieval Augmented Generation (RAG) pipelines, including vector database integration for contextual retrieval.
Work with multi modal AI systems across computer vision, audio, and natural language domains.
Employ synthetic data generation and digital twinning techniques (GANs, diffusion models, or simulation based) to create robust datasets for edge cases.
Develop GPU accelerated and low level system code in C, C++, or Rust for performance critical operations.
Optimize model execution for distributed and resource constrained environments, ensuring reliability under variable connectivity conditions.
Collaborate cross functionally with Infrastructure, MLOps, and Security teams to deliver secure, compliant, and high performance AI solutions for government partners.