AI/ML Automation Engineer

Argo Cyber Systems•Arlington, VA

5d•Hybrid

About The Position

Argo Cyber Systems is seeking an experienced AI/ML Automation Engineer to architect and implement intelligent automation capabilities that enhance cyber operations, malware analysis, digital forensics, and mission decision-making. This role combines machine learning engineering, generative AI, cloud-native development, and workflow automation to build scalable AI solutions supporting national cybersecurity missions. The AI/ML Automation Engineer serves as the senior technical lead responsible for designing, developing, deploying, and optimizing AI-driven capabilities across cybersecurity operations. You will collaborate with cyber analysts, data scientists, software engineers, cloud architects, and mission stakeholders to implement advanced machine learning models, Large Language Model (LLM) integrations, Retrieval-Augmented Generation (RAG) solutions, autonomous agent workflows, and intelligent automation supporting operational cyber missions. This position offers the opportunity to shape next-generation AI capabilities used in support of incident response, malware analysis, threat intelligence, digital forensics, and cybersecurity modernization.

Requirements

U.S. Citizenship
Active Secret Security Clearance
Ability to obtain DHS Entry on Duty (EOD) Suitability
Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, Computer Engineering, or related STEM field
Five (5) or more years of professional experience developing AI, ML, automation, or data engineering solutions
Strong Python development experience
Experience with Databricks AI Platform
Experience implementing machine learning pipelines
Experience integrating Large Language Models (LLMs)
Experience with Amazon Bedrock and AWS AI services
Experience building REST APIs and cloud-native applications
Experience with Git, CI/CD pipelines, and software development best practices
Strong analytical, problem-solving, and communication skills

Nice To Haves

Active TS/SCI Clearance
Experience supporting DHS, CISA, NSA, FBI, DoD, or Intelligence Community programs
Experience implementing Generative AI solutions
Experience developing autonomous AI agents
Experience building RAG architectures
Experience with LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel, or similar AI orchestration frameworks
Experience with vector databases (Pinecone, FAISS, ChromaDB, OpenSearch Vector Engine)
Experience supporting malware analysis, digital forensics, or threat intelligence
Experience implementing MLOps pipelines
Experience deploying AI into production cloud environments
AWS Certified Machine Learning – Specialty
AWS Certified AI Practitioner
AWS Solutions Architect – Professional
Databricks Certified Machine Learning Professional
Microsoft Azure AI Engineer Associate
Google Professional Machine Learning Engineer
CSSLP
DoD 8140 IAT Level III
CISSP (Preferred)
GIAC Certified Forensic Analyst (GCFA) (Preferred)

Responsibilities

Design, develop, and deploy enterprise AI and machine learning solutions supporting cybersecurity operations.
Develop intelligent automation using LLMs, foundation models, and autonomous AI agents.
Build Retrieval-Augmented Generation (RAG) pipelines for secure knowledge retrieval.
Design prompt engineering strategies and optimize AI model performance.
Develop feature engineering, data preparation, and ML training pipelines.
Support model evaluation, tuning, validation, and continuous improvement.
Design end-to-end automation workflows for cyber operations.
Automate malware collection, detonation, classification, and analysis.
Build AI-driven orchestration supporting incident response and digital forensics.
Develop APIs enabling secure data exchange between cyber platforms.
Automate repetitive analytical processes to improve operational efficiency.
Design cloud-native AI solutions using AWS services.
Develop AI integrations utilizing Amazon Bedrock and foundation models.
Implement scalable ML pipelines using Databricks and cloud data platforms.
Deploy containerized AI workloads using Docker and Kubernetes.
Build CI/CD pipelines supporting rapid AI model deployment.
Design and maintain scalable data ingestion pipelines.
Build data transformation, normalization, and enrichment workflows.
Develop streaming data integrations supporting AI workloads.
Optimize data quality, governance, and operational performance.
Support structured, semi-structured, and unstructured data processing.
Develop AI-enabled malware analysis capabilities.
Support AI-driven threat intelligence enrichment.
Build detection engineering automation.
Develop machine learning capabilities supporting digital forensics.
Assist cyber analysts by integrating AI decision-support capabilities into operational workflows.
Provide technical leadership for AI modernization initiatives.
Mentor engineers on AI engineering best practices.
Collaborate with cybersecurity SMEs and software development teams.
Develop architecture documentation, design artifacts, and technical standards.
Present AI capabilities to executive leadership and government stakeholders.