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About This Role
Cloud / DevSecOps Engineer – AI\-Enabled Cloud Automation
Company: BizTech Fusion
Location: 100% Remote (USA)
Experience: 10–12\+ Years
Job Type: Contract
About the Role
BizTech Fusion is seeking an experienced Cloud / DevSecOps Engineer – AI\-Enabled Cloud Automation for one of our clients. The ideal candidate will have strong expertise in AWS Cloud, DevSecOps, Infrastructure as Code (IaC), CI/CD automation, containerization, Kubernetes, and AI\-enabled cloud engineering.
The candidate will be responsible for designing, implementing, securing, and automating cloud infrastructure while ensuring scalable, reliable, and compliant environments that support modern application development.
Required Qualifications
- 10–12\+ years of experience in Cloud Engineering, DevSecOps, Cloud Infrastructure, or related roles.
- Must be a U.S. Citizen (No Dual Citizenship).
- Must be eligible to obtain and maintain a Public Trust Clearance.
- Experience supporting enterprise\-scale cloud environments or government/public sector projects.
- Strong communication, troubleshooting, and problem\-solving skills.
Required Technical Skills
- Strong hands\-on experience with AWS Cloud Services, including EC2, Lambda, S3, RDS, API Gateway, SQS, IAM, CloudWatch, CloudFormation, VPC, ECS/EKS.
- Experience developing and managing Infrastructure as Code (IaC) using Terraform and AWS CloudFormation.
- Strong knowledge of DevSecOps methodologies, secure software development lifecycle (SSDLC), security automation, vulnerability management, and compliance automation.
- Experience designing and managing CI/CD pipelines using Jenkins, GitHub Actions, AWS CodePipeline, or similar tools.
- Hands\-on experience with Docker, Kubernetes, and Amazon EKS.
- Strong scripting experience with Python and Shell/Bash.
- Experience with Git, GitHub, Agile/Scrum methodologies.
- Understanding of cloud\-native application technologies including Java, Spring Boot, React.js, REST APIs, and Microservices is preferred.
AI\-Enabled Cloud Engineering Experience
Candidates must have practical experience using AI\-assisted development and automation tools such as:
- GitHub Copilot
- Amazon Q
- ChatGPT
- Claude
- Other enterprise\-approved AI assistants
Experience leveraging AI tools for:
- Infrastructure design and architecture planning.
- Infrastructure as Code generation.
- CI/CD pipeline development.
- Security automation.
- Cloud documentation.
- Code reviews.
- Troubleshooting.
- Automation scripting and operational support.
Candidates should be able to demonstrate how AI tools have improved cloud engineering productivity while maintaining security, quality, and production readiness.
Key Responsibilities
- Design, build, and maintain secure AWS cloud environments.
- Develop and manage Infrastructure as Code solutions using Terraform and CloudFormation.
- Automate cloud provisioning, deployments, and operational processes.
- Design and maintain CI/CD pipelines for continuous delivery.
- Implement DevSecOps best practices throughout the software delivery lifecycle.
- Build and manage containerized environments using Docker and Kubernetes.
- Implement cloud security controls and compliance standards.
- Monitor cloud infrastructure performance, availability, and reliability.
- Configure logging, monitoring, and alerting solutions.
- Collaborate with development, security, and operations teams.
- Troubleshoot infrastructure, deployment, and production issues.
- Create technical documentation and operational runbooks.
- Support production releases, disaster recovery, and business continuity activities.
Preferred Qualifications
- Experience with federal government or large enterprise cloud modernization projects.
- Experience with cloud migration initiatives.
- Knowledge of Zero Trust Architecture and security frameworks.
- Experience with AWS Well\-Architected Framework.
- Experience with cloud cost optimization and monitoring tools.
- Kubernetes administration experience.
- AWS or DevOps\-related certifications are highly desirable.
If you are an experienced Cloud/DevSecOps Engineer with expertise in AWS, automation, Kubernetes, Infrastructure as Code, and AI\-enabled cloud engineering, we encourage you to apply and join BizTech Fusion on impactful technology initiatives.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At BizTech Fusion, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
BizTech Fusion AI Hiring
BizTech Fusion has 4 open AI roles right now. They're hiring across AI Architect, AI/ML Engineer. Positions span Austin, TX, US, Dallas, TX, US, Remote, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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