Hybrid | AI/ML Engineer — $70–$200/hour

24-MAG; Washington (District of Columbia); Hybrid; USD 96,000 - 276,000; Full time

Benefits offered by this job

Hybrid work model

Job summary

24-MAG is seeking an experienced AI/ML Engineer to design and deploy production-grade AI systems using LLMs, RAG, and agentic orchestration in a hybrid, security-focused environment.

The role emphasizes building scalable data infrastructure, secure cloud deployments, and cross-functional collaboration with product, security, and data teams in mission-critical contexts.

Qualifications

• Strong proficiency in Python for production AI/ML development.
• Hands-on experience building production systems using LLMs and RAG.
• Experience with multi-agent orchestration and tool use.
• Familiarity with LangGraph, LangChain, or similar frameworks.
• Cloud AI service expertise and secure deployment experience.
• Experience in government or regulated environments is advantageous.

Responsibilities

• Design, implement, and optimise production-grade AI/ML systems.
• Build apps using large language models, retrieval-augmented generation, and prompt engineering.
• Design reliable retrieval and grounding workflows.
• Develop multi-agent systems and orchestration patterns.
• Deploy AI solutions in secure cloud environments.
• Build REST APIs and SDK integrations.
• Ensure secure coding and CI/CD in production.

Skills

Python

LLMs

RAG

Prompt engineering

Multi-agent orchestration

LangGraph

LangChain

Cloud AI services

REST APIs

CI/CD

Secure coding

Communication

Tools

LangGraph

LangChain

AWS GovCloud

Google GovCloud

Azure IL5

Vertex AI

AWS Bedrock

Job description

We are sharing a full-time opportunity for an experienced AI/ML Engineer with strong expertise in Python, large language models, retrieval‑augmented generation, agentic systems, cloud AI infrastructure, and production‑grade machine learning to contribute to secure, mission‑critical AI initiatives.

The role will focus on designing and deploying advanced AI systems using LLMs, RAG, multi‑agent orchestration, secure cloud platforms, and robust data infrastructure. The ideal candidate combines deep hands‑on engineering ability with strong systems thinking, production experience, and comfort working in highly regulated or security‑sensitive environments.

LLM & RAG Systems

• Design, implement, and optimise production‑grade AI/ML systems
• Build applications using large language models, retrieval‑augmented generation, and prompt engineering
• Evaluate and improve model behaviour across complex production use cases
• Design reliable retrieval and grounding workflows
• Apply strong engineering standards to performance, scalability, and maintainability

Agentic AI & Multi‑Agent Orchestration

• Develop and orchestrate multi‑agent systems using modern agent frameworks
• Work with platforms such as LangGraph, LangChain, or comparable tooling
• Design tool‑use workflows and agent interaction patterns
• Build reliable control logic for complex multi‑step AI tasks
• Evaluate agent behaviour, failure modes, and system‑level trade‑offs

Secure Cloud AI Infrastructure

• Deploy and integrate AI solutions within secure cloud environments
• Work with platforms such as AWS GovCloud, Google GovCloud, Azure IL5 , Vertex AI, and AWS Bedrock
• Design infrastructure appropriate for sensitive or highly regulated applications
• Apply secure engineering practices across development and deployment
• Collaborate with security teams to ensure technical solutions align with environment‑specific requirements

Data Pipelines & Knowledge Infrastructure

• Build and maintain robust data pipelines for AI training and inference
• Design and manage ETL workflows
• Develop metadata catalogues, ontologies, and structured knowledge representations
• Improve data quality, lineage, and accessibility across AI systems
• Support reliable ingestion, transformation, and retrieval workflows

APIs, Integrations & Production Engineering

• Build and maintain REST APIs and SDK integrations
• Connect AI models, external systems, and data services through reliable interfaces
• Apply modern software engineering and secure coding standards
• Implement and maintain CI/CD workflows
• Support deployment, monitoring, and operational reliability of production AI systems

Cross‑Functional Technical Leadership

• Collaborate closely with product, security, engineering, and data teams
• Document technical decisions and architecture clearly
• Communicate complex technical concepts to both technical and non‑technical stakeholders
• Contribute to engineering standards and architecture decisions
• Help translate mission requirements into secure and scalable technical solutions

Ideal Profile

• Strong proficiency in Python for production AI/ML development
• Hands‑on experience building production systems using LLMs, RAG, and prompt engineering
• Experience with multi‑agent orchestration, tool use, or agentic AI systems
• Familiarity with LangGraph, LangChain, or comparable orchestration frameworks
• Strong understanding of cloud AI services and secure deployment environments
• Experience with AWS GovCloud, Google GovCloud, Azure IL5 , Vertex AI, AWS Bedrock, or related platforms is highly relevant
• Background building data pipelines, ETL systems, metadata catalogues, or ontologies
• Strong experience with REST APIs and SDK integrations
• Understanding of secure coding and modern DevOps practices, including CI/CD
• Strong written and verbal communication skills
• Experience with government, defence, highly regulated, or compliance‑sensitive environments is advantageous
• Familiarity with enterprise AI platforms such as Anthropic for Gov, OpenAI Enterprise, Gemini Enterprise, or Grok Enterprise is beneficial
• Knowledge of advanced API development and metadata or context‑management platforms is a plus

Engagement Details

• Full‑time engagement
• Hybrid
• Compensation: $70--$200/hour
• Work will involve production AI/ML engineering, LLM systems, RAG, agentic workflows, cloud deployment, data infrastructure, and secure technical integration
• Experience in government or highly regulated environments is particularly relevant
• Strong collaboration with product, security, engineering, and data teams is central to the role
• Technical scope may include mission‑critical systems and environments with elevated security or compliance requirements
• Role responsibilities and technical priorities may evolve as projects and deployment requirements change