AI Engineer | Senior

The Amatriot Group; Chantilly (VA); On-site; USD 160,000 - 180,000; Full time

Job summary

The Amatriot Group in Chantilly, VA is seeking an AI Engineer (SME) to design and deploy scalable AI/ML models, pipelines, and microservices for an in-house application.

You will work across data architectures, enterprise systems, and distributed infrastructure to deliver production-grade GenAI solutions with strong governance and security requirements. A Bachelor’s degree and 8+ years of AI/ML engineering experience are required.

Qualifications

• Bachelor’s degree required.
• Minimum of 8 years of specialized AI/ML engineering experience.
• Hands-on experience deploying production GenAI/LLM systems, embeddings, and vector databases.
• Practical experience with MLOps tools and cloud platforms.
• Background in NLP, computer vision, or reinforcement learning.
• Experience working in agile engineering environments.

Responsibilities

• Build scalable, production-ready AI/ML/GenAI models, pipelines, and microservices.
• Develop training, evaluation, and inference pipelines in Python using ML frameworks.
• Deploy and manage models using MLOps, CI/CD, containerization, and monitoring frameworks.
• Implement vector databases, embeddings, LLM applications, agentic AI, and tool-calling workflows.
• Optimize models for accuracy, latency, cost, and scalability across distributed environments.

Skills

Advanced Python

Distributed systems

NLP

Computer vision

Reinforcement learning

AWS/Azure/GCP

Education

Bachelor’s degree

Tools

MLflow

Azure ML

Vertex AI

Kubernetes

Docker

PyTorch

TensorFlow

LanceDB

Job description

Location: Chantilly, VA

Security Clearance: Active TS or higher [Required] w/ ability to obtain CI Poly

Job Type: Full-Time

Target Salary Range*: $160,000 - $180,000

• This represents the potential salary range for this position depending on education level, years of experience and/or certifications in addition to other position specific requirements which may impact salary
  
  

Position Overview

The AI Engineer (SME) builds and deploys scalable, production-ready AI, machine learning, and generative AI models, pipelines, and microservices. This role supports an in-house, custom-developed application, integrating advanced AI capabilities, enterprise data architectures, and distributed infrastructure. The SME also provides technical mentorship, promotes engineering standardization, and ensures alignment with AI governance and security requirements.

Key ResponsibilitiesAI/ML Development and Deployment

• Build scalable, production-ready AI/ML/GenAI models, pipelines, and microservices.
• Develop training, evaluation, and inference pipelines in Python using PyTorch, TensorFlow, or scikit-learn.
• Deploy and manage models using MLOps, CI/CD, containerization, and monitoring frameworks.
• Implement vector databases, embeddings, LLM applications, agentic AI, and tool-calling workflows.
• Optimize models for accuracy, latency, cost, and scalability across distributed environments.
  
  

Advanced AI and Multimodal Engineering

• Orchestrate multiple models and modalities to support reasoning, perception, memory, and tool execution.
• Implement temporal, spatial, geographic, quantitative, and computer vision AI capabilities.
• Develop named entity recognition (NER), contextual entity extraction, and entity resolution capabilities.
• Implement multistep retrieval and structured output generation.
• Support complex data parsing and streamlined ingestion.
• Provide detailed source citations and traceable analytic outputs.
  
  

Data Architecture and ETL

• Orchestrate vector, graph, and multiple database systems.
• Create, schedule, and optimize automated extract, transform, and load (ETL) pipelines.
• Implement attribute-based access control (ABAC) at the data layer.
• Track data provenance and end-to-end lineage.
• Support schema-aligned ingestion and normalization of diverse structured and unstructured data.
  
  

Enterprise Infrastructure and Cross-Domain Solutions

• Support air-gapped, hybrid, and on-premises operational environments.
• Manage container orchestration and distributed systems using Kubernetes.
• Support locally hosted GPU servers and hyperconverged nodes.
• Support multi-tenant cross-domain solutions (CDS).
  
  

Application Development and Integration

• Develop user interfaces, backend services, and middleware for an in-house, custom-developed application.
• Use TypeScript, React, Python, FastAPI, LanceDB, Lance Graph, PostgreSQL, Temporal.io, Docker, and Kubernetes.
• Integrate reasoning components, retrieval systems, databases, and AI/GenAI services.
• Apply modern programming languages and frameworks to application development and integration.
  
  

Governance and Technical Leadership

• Ensure alignment with AI governance, documentation, ethical compliance, and security posture requirements.
• Provide technical mentorship and drive engineering standardization across teams.
  
  

QualificationsEducation

• Bachelor’s degree required.
  
  

Experience

• Minimum of 8 years of specialized AI/ML engineering experience.
• Hands-on experience deploying production GenAI/LLM systems, embeddings, and vector databases.
• Practical experience with MLOps tools such as MLflow, Azure ML, or Vertex AI.
• Background in natural language processing (NLP), computer vision, or reinforcement learning.
• Experience working in agile engineering environments.
  
  

Skills

• Advanced Python skills and a strong understanding of algorithms, data structures, and distributed systems.
• Proficiency with PyTorch, TensorFlow, and scikit-learn.
• Proficiency with cloud platforms such as AWS, Azure, or GCP.
• Advanced AI, GenAI, multimodel orchestration, and multimodal engineering capabilities.
• Skills in data architecture, automated ETL, database orchestration, ABAC, and data lineage.
• Technical capabilities supporting air-gapped, hybrid, and on-premises environments, Kubernetes, GPU infrastructure, and multitenant CDS.
• UI, backend, and middleware engineering skills using the specified application technologies.
  
  

Preferred Qualifications

• Experience in federal, defense, or highly regulated environments.
• Knowledge of ethical AI, governance frameworks, and secure development practices.
• Skills in large-scale optimization, GPU acceleration, or distributed training.
• Experience building APIs, microservices, and automated ML workflows.