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

24-Mag Llc; Washington, Northern (District of Columbia, KY); Hybrid; USD 96,000 - 276,000; Full time

Benefits offered by this job

Hybrid work arrangement

Competitive hourly rate

Job summary

24-Mag LLC is seeking an experienced AI/ML Engineer in a full-time hybrid role to design and deploy production-grade AI systems using LLMs, RAG, and agentic workflows for highly regulated environments. You will build data pipelines, secure cloud infrastructure, APIs, and CI/CD, working with LangGraph, LangChain, AWS GovCloud, Vertex AI, and other platforms.

Candidates should have strong Python skills, hands-on production experience, and ability to collaborate with security and product teams on

Qualifications

• Hands-on engineering experience building production AI/ML systems using LLMs, RAG, and prompt engineering.
• Strong systems thinking with production-grade design, security-sensitive deployment, and regulatory awareness.
• Experience integrating REST APIs, data pipelines, and secure cloud infrastructure.

Responsibilities

• Design, implement, and optimise production-grade AI systems.
• Build applications using LLMs, RAG, and prompt engineering.
• Develop and orchestrate multi-agent workflows and tool-use patterns.
• Deploy AI solutions within secure cloud environments and CI/CD pipelines.
• Collaborate with security, product, and data teams to ensure reliability and compliance.

Skills

Python

LLMs & RAG

Multi-agent orchestration

REST APIs

CI/CD

Cloud deployment

Tools

LangGraph

LangChain

AWS GovCloud

Vertex AI

AWS Bedrock

Google GovCloud

Azure IL5+

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.

Key Responsibilities

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

About the Platform

This opportunity is available through 24-MAG LLC. We connect experienced professionals with remote consulting opportunities across technical, evaluation, and project-based workstreams.