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About This Role
Build What Matters at RevStar
Position: Data & AI Forward Deployed Engineer (FDE)
- Reports To: Product Manager/Engineering Lead
- Location: Remote (AZ, DC, FL, GA, IA, IL, ME, NY, NJ, SC, TX, VA)
- Employment Type: Full-Time W2
Job Summary
At RevStar, we build cutting-edge cloud, data, and Generative AI solutions that transform how enterprise organizations operate. As an Advanced AWS Consulting Partner, we don’t just deliver code—we solve complex business problems, modernize legacy infrastructure, and deploy production-grade cloud native and AI applications that drive real business value. Above all, our team operates on three core principles:
- Self-Mastery: We hold a high bar for how we think, communicate, and improve.
- Ownership: We own outcomes, not just effort.
- Shared Destiny: We rise or fall together.
We are seeking a Data & AI Forward Deployed Engineer (FDE) to join our Digital Solutions Practice. In this highly visible role, you will operate embedded directly at the intersection of enterprise cloud architecture, hands-on engineering, and client-facing delivery. As an FDE, you are both a master cloud builder and a trusted technical advisor. You will own the full delivery lifecycle for high-impact enterprise accounts—architecting resilient cloud-native environments, modernizing legacy infrastructure, and deploying state-of-the-art AWS Generative AI (Amazon Bedrock, Q, SageMaker) architectures. Simultaneously, you will drive AWS adoption and consumption growth by identifying new workloads, building POCs, and driving strategic technical alignment with client executive leadership.
Key Responsibilities
- Hands-On Cloud Architecture & Implementation: Design, build, and deploy secure, scalable, and resilient cloud architectures on AWS using Infrastructure as Code (Terraform, AWS CDK, CloudFormation) and modern microservices/serverless patterns (Lambda, ECS/EKS, API Gateway, DynamoDB).
- Enterprise Customer Delivery: Serve as the primary technical leader on strategic enterprise client engagements. Partner with CIOs, CTOs, VPs of Infrastructure/Engineering, and client development teams to translate complex business goals into high-performing AWS solutions.
- Cloud Migration & Application Modernization: Lead large-scale migrations from on-premises data centers or legacy platforms to AWS (using AWS Application Migration Service, Database Migration Service, etc.) and re-architect legacy monolithic applications into modern serverless or containerized architectures.
- AWS Generative AI & Machine Learning: Architect and deploy enterprise GenAI and ML workloads leveraging Amazon Bedrock, SageMaker, Vector Databases (OpenSearch, Pinecone, pgvector), and custom RAG implementations.
- Drive Platform Adoption & Growth: Partner with RevStar account leadership to identify new, high-value cloud workloads within client accounts, driving AWS consumption expansion and clear ROI for client executive stakeholders.
- Technical Mentorship & Practice Building: Coach and mentor Cloud Engineers and Solutions Architects. Drive internal RevStar accelerators, reusable Terraform modules, AWS Well-Architected Review frameworks, and cloud delivery best practices.
Requirements
Qualifications & RequirementsTechnical Expertise:* 6+ years of hands-on experience in Cloud Architecture, DevOps/SRE, or Software Engineering with a focus on AWS.
- 3+ years in a client-facing consulting, Solutions Architecture, or Forward Deployed Engineering role.
- Deep AWS Core Expertise: Mastery across core AWS services (IAM, VPC, EC2, ECS/EKS, Lambda, S3, RDS, DynamoDB, CloudFront, Route 53).
- Infrastructure as Code (IaC) & CI/CD: Expert-level proficiency in Terraform, AWS CDK, or CloudFormation, along with automated CI/CD pipelines (GitHub Actions, GitLab CI, AWS CodePipeline).
- AWS GenAI & Data Services: Practical experience with Amazon Bedrock, SageMaker, Glue, Redshift, or OpenSearch for building data platforms and AI solutions.
- AWS Certifications: AWS Certified Solutions Architect – Professional and/or AWS Certified DevOps Engineer – Professional required. Specialty certifications (Security, Machine Learning, Data Analytics) are a plus.
Client & Business Acumen:* Enterprise Track Record: Proven experience leading technical delivery for Fortune 500 or mid-market enterprise clients (Healthcare, Financial Services, Retail, or SaaS).
- Consulting & Communication Skills: Outstanding verbal and written communication; adept at whiteboarding complex architectures, building working POCs/demos, and presenting to C-level executives.
- Growth Mindset: Ability to spot technical expansion opportunities, conduct AWS Well-Architected Reviews, and advocate for cloud modernization to expand account footprint.
Benefits
- Paid Time Off – Take the time you need to recharge and stay productive.
- Remote-First Working Environment – Collaborate from anywhere while staying connected with our global team.
- Comprehensive Health Coverage – Medical, Dental, Vision
- 401(k) Retirement Plan – Plan for your future with access to a company-sponsored 401(k) program.
- Annual Learning & Development Stipend – Invest in your skills with conferences, certifications, or courses.
- Peer Mentorship & Coaching – Learn from experienced engineers, product managers, and architects to accelerate your growth.
- Professional Growth Opportunities – Exposure to cutting-edge AWS GenAI, data, and cloud technologies across diverse industries.
- Company Outings & Volunteer Opportunities – Build relationships and give back to the community.
- Collaborative, Innovative Culture – Work alongside top talent in a fast-paced, supportive environment that values curiosity and initiative.
Equal Opportunity Employment
At RevStar, we don’t just accept differences — we celebrate them, we support them, and we thrive on them for the benefit of our employees, our customers, and our community. RevStar is proud to be an equal opportunity workplace.
Reasonable Accommodations
RevStar is committed to providing access, equal opportunity and reasonable accommodation for individuals with disabilities in employment, its services, programs, and activities. To request reasonable accommodation, contact HR at [email protected].
E-Verify
Our company participates in E-Verify to confirm work authorization for all newly hired U.S. employees; this process applies strictly to direct W-2 hires and is not applicable to independent contractor roles.
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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Revstar, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Revstar AI Hiring
Revstar has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Washington, DC, US, AZ, US.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 median, while Prompt Engineer roles sit at $140,000. 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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