AI/ML Automation Engineer

We need to output HTML fragment with sections: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections if posting has real specific content. We need to extract content. Given posting includes: - Location: Government Facility (Hybrid/Onsite) - Security Clearance: Active Secret Clearance Required (TS/SCI Preferred) - Customer: U.S. Department of Homeland Security (DHS) - Employment Type: Full-Time - About Argo Cyber (company description) - Position Overview - Primary Responsibilities (list) - Required Qualifications (list) - Preferred Qualifications (list) - Desired Technical Skills (list) - Desired Certifications (list) - What Success Looks Like Within Your First Year (list) - Why Join Argo Cyber? (list) - Background & Drug Screening Disclaimer (boilerplate to remove) - Job Posted by ApplicantPro (boilerplate) We need to map to sections: - About the role: could be Position Overview? Or About Argo Cyber? The instruction: use

for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we need to decide which content goes where. Typically: - About the role: overview of role (Position Overview) - Responsibilities: Primary Responsibilities - Requirements: maybe Required Qualifications? Or could be Requirements (mandatory) - Qualifications: maybe Preferred Qualifications? Or combine? The instruction says separate sections Requirements and Qualifications. We'll put Required Qualifications under Requirements, and Preferred Qualifications under Qualifications. - Skills: Desired Technical Skills (maybe also certifications? Could be separate but we only have Skills section; we could include certifications under Skills or maybe not include if not enough. But we have Desired Certifications list; could be part of Skills or maybe we omit if not enough? The instruction: include a section ONLY when posting has real specific content. We have content for certifications, so we could include under Skills (maybe combine). But better to keep Skills for technical skills, and maybe we could add a separate Certifications section? Not in allowed headings list. So we must fit into allowed headings. Could put certifications under Skills as part of skill set. Or we could omit if we think it's not enough? But we have content; we should include it somewhere. Since Skills is allowed, we can include both technical skills and certifications under Skills. - Benefits: The posting mentions "Competitive compensation, comprehensive benefits, technical training, and opportunities to shape the future of AI-driven cyber operations." That's under Why Join Argo Cyber? Actually that paragraph includes benefits. Also earlier: "Competitive compensation, comprehensive benefits, technical training, and opportunities to shape the future of AI-driven cyber operations." That's benefits. So we can create Benefits section with that content. - Pay: Not specified; no salary info. So we should NOT include Pay section. - Schedule: Not specified; only Employment Type: Full-Time. Could we consider Schedule as Full-Time? The instruction: never create Pay/Schedule/Benefits sections unless posting actually states them. It does state Employment Type: Full-Time, which is schedule info. So we can include Schedule section with Full-Time (maybe also Hybrid/Onsite). But location also given. However schedule is about work schedule; we have Full-Time. Could also mention Hybrid/Onsite under Location but not schedule. We'll put Schedule: Full-Time (Hybrid/Onsite maybe). But we should not invent; we have Employment Type: Full-Time. Location is separate but not a heading. We could include location in a short p under maybe About the role? But we should not repeat job title. We could include location and employment type in a short p under About the role or maybe as metadata. The instruction: do NOT put Job ID / requisition numbers as headings — fold minor metadata into a short

or drop it. So we can include location and employment type in a short p under About the role or maybe at top. But we need to avoid duplication. We need to preserve ALL substantive information; do not invent or summarize away detail. So we need to keep all details from the posting, but we can reformat. We must remove boilerplate: cookie/navigation/legal/EEO/equal-opportunity/scam-warning, duplicated lines, application instructions. The "Background & Drug Screening Disclaimer" is legal boilerplate; we should remove it. Also "Job Posted by ApplicantPro" remove. We need to keep all substantive content: company description, position overview, responsibilities, required qualifications, preferred qualifications, desired technical skills, desired certifications, what success looks like, why join Argo Cyber (benefits), location, security clearance, customer, employment type. We need to output only HTML fragment, no /, no markdown. We'll structure:

About the role

Location: Government Facility (Hybrid/Onsite). Security Clearance: Active Secret Clearance Required (TS/SCI Preferred). Customer: U.S. Department of Homeland Security (DHS). Employment Type: Full-Time.

[Company description]

[Position Overview]

But we need to avoid duplication; we could combine company description and position overview into About the role section. Better: About the role includes a brief description of the role and company. We'll include the company overview and position overview. Then Responsibilities:

Responsibilities

with each responsibility line. We have many responsibilities grouped under headings: AI & Machine Learning Engineering, Intelligent Automation, Cloud AI Engineering, Data Engineering, Cybersecurity AI, Collaboration & Leadership. We need to preserve all detail; we can either keep subheadings as inside
• or just list each bullet. The instruction: use
  • for any list of duties/requirements/benefits. It didn't forbid subheadings inside li. We'll keep each bullet as
  • and maybe include the heading as part of the text. We'll extract each bullet: From Primary Responsibilities: AI & Machine Learning Engineering: - 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. Intelligent Automation: - 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. Cloud AI Engineering: - 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. Data Engineering: - 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. Cybersecurity AI: - 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. Collaboration & Leadership: - 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. We'll put each as
  • . Requirements: Required Qualifications list: - 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 We'll put each as
  • . Qualifications: Preferred Qualifications list: - 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 We'll put each as
  • . Skills: Desired Technical Skills list (maybe also certifications). We'll include the list under Skills. Desired Technical Skills bullet list includes many items grouped by categories. We'll flatten each item as
  • . Also include Desired Certifications list as part of Skills (maybe separate bullet). We'll include them. Desired Technical Skills: - Artificial Intelligence - Large Language Models (LLMs) - Generative AI - Retrieval-Augmented Generation (RAG) - Prompt Engineering - AI Agents - Agentic AI - Model Fine-Tuning - Reinforcement Learning - Natural Language Processing (NLP) - Embedding Models - Machine Learning - TensorFlow - PyTorch - Scikit-learn - XGBoost - ML Pipelines - Feature Engineering - Model Evaluation - Model Deployment - Cloud AWS - Amazon Bedrock - SageMaker - Lambda - ECS - EKS - S3 - IAM - CloudWatch - Databricks - Data Engineering - Spark - Kafka - Kinesis - Airflow - Prefect - SQL - NoSQL - Data Lakes - ETL/ELT - Vector Databases - Software Engineering - Python - FastAPI - REST APIs - Docker - Kubernetes - Git - GitHub Actions - Jenkins - Terraform - Cybersecurity - Malware Analysis - Threat Intelligence - Incident Response - Digital Forensics - MITRE ATT&CK - Detection Engineering - SIEM Integration - Security Automation Desired Certifications: - 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) We'll include these as
  • under Skills. Benefits: from Why Join Argo Cyber? paragraph: "Design next-generation AI capabilities supporting national cybersecurity.Work with cutting-edge Generative AI, LLMs, and autonomous agent technologies.Collaborate with elite cybersecurity engineers, AI researchers, and Federal mission partners.Influence AI modernization across critical government missions.Competitive compensation, comprehensive benefits, technical training, and opportunities to shape the future of AI-driven cyber operations." We'll extract benefits: Competitive compensation, comprehensive benefits, technical training, opportunities to shape the future of AI-driven cyber operations. Also maybe the other