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About This Role
Cinteot Inc. is a small IT services company that specializes in cybersecurity, Big Data/databases, software development, systems testing, STIG Compliance training, closed\-circuit television/security cameras and access controls, and construction/facilities.
We are a Woman\-owned, SBA Certified 8(a), and HUBZone company.
Job Overview:
The AI Platform Engineer builds, hardens, and helps operate the enterprise GenAI/agentic platform that enables teams to safely develop and run AI agents at scale. This role owns the platform “foundations”—including LLM gateway \& model routing, vector/knowledge base infrastructure, CI/CD and IaC automation, observability, and cost/usage management across use cases —so Dev teams can deliver agents quickly under consistent governance and controls. This role focuses on shared platform capabilities (as distinct from use\-case AI engineering) and partners closely with CoE product/ops enablement and governance stakeholders to keep the platform scalable, compliant, and easy to onboard new agents and tools.
Key Responsibilities:
- Engineer and maintain the platform’s foundational capabilities: LLM gateway, model routing, shared compute/infrastructure, and platform governance controls.
- Onboard and support new platform capabilities and tools used by agents (e.g., adding/maintaining supported models and agent tooling standards).
- Provision and manage platform resources needed to scale agent development (e.g., provisioning Bedrock Knowledge Bases/vector\-backed retrieval components where applicable).
- Build and maintain Terraform (or equivalent) IaC to create/update platform resources and dashboards; keep deployments repeatable across environments.
- Own and operate platform pipelines: executing Terraform \+ backend/frontend pipelines, troubleshooting failed deployments, and improving delivery reliability.
- Provide “platform support” for agent teams by diagnosing platform\-related issues (e.g., deployment/update failures, agent creation problems, missing/incorrect results, conversation memory issues).
- Implement and maintain monitoring for performance/quality and agent behavior over time (including benchmark testing and evaluation monitoring such as “Bedrock Eval” where applicable).
- Own platform cost \& usage management practices (budget signals, usage metrics, reporting, optimization recommendations).
- Collaborate with Security/Architecture/Governance partners to ensure platform controls align to enterprise policies and approved operating patterns (e.g., access controls, safe enablement of who can create what kinds of agents).
Required Qualifications:
- Bachelor’s degree in computer science, Engineering, Information Systems, or related technical discipline, or equivalent experience.
- Demonstrated experience designing and operating shared application or platform services in an enterprise environment.
- Experience with CI/CD pipelines, automation, and infrastructure provisioning.
- Working knowledge of modern AI/ML or GenAI platforms and cloud\-based services.
- Strong understanding of operational reliability, monitoring, and support practices.
- Experience supporting AI, ML, or GenAI platforms in regulated industries (e.g., healthcare, insurance, financial services).
- Familiarity with agent\-based architectures, LLM model integration, and AI platform patterns.
- Experience with cloud cost management, usage monitoring, or FinOps practices.
- Professional cloud or AI\-related certifications (AWS, Azure, or equivalent).
Benefits:
- Flexible remote work environment.
- Comprehensive health benefits and wellness programs.
- Opportunities for professional development and career growth.
Company Culture:
At Cinteot, we foster an inclusive and collaborative environment where innovation thrives. We believe in empowering our employees to take ownership of their work and contribute to meaningful projects that impact our community and industry.
Benefits:
- Complete Insurance Coverage
- Blue Cross Medical, Delta Dental, Vision, Life
- 401k with Company Contribution
- Tuition Reimbursement
- Generous Paid Time Off (including your birthday!)
Cinteot is an Equal Opportunity Employer
All qualified applicants will receive consideration for employment without regard to race, sex, color, religion, sexual orientation, gender identity, national origin, protected veteran status, or on the basis of disability.
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 Cinteot, 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.
Cinteot AI Hiring
Cinteot has 3 open AI roles right now. They're hiring across AI/ML Engineer, Data Engineer. Based in Newark, NJ, 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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