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About This Role
Job Description
The Opportunity: Are you a true Cloud Builder? We are looking for a Senior Azure Infrastructure Engineer who doesn't just monitor what someone else built, but actively architects, provisions, and automates high\-performance cloud environments.
The Mission: Our infrastructure operations run a highly competent, nearly 100% Azure environment. As the Senior Engineer and designated "Number Two" to the Infrastructure Manager, you will bring a fresh set of eyes to our topology to aggressively accelerate our cloud maturity.
You will lead the transition from legacy VM dependencies to Azure\-native PaaS solutions, design automated pipelines from the ground up, and serve as the core infrastructure engineering partner to our software development teams as they scale enterprise AI pipelines (including Copilot governance, Model Context Protocol (MCP) connections, and Snowflake Cortex integrations).
Key Responsibilities
- Cloud Architecture \& Optimization: Review our current cloud footprint to identify legacy virtual machine clusters and refactor them into high\-efficiency, native Azure PaaS environments and App Services.
- Infrastructure Automation (IaC): Establish our internal scripting and automation capabilities from scratch. You will lead the transition away from manual deployments by implementing PowerShell automation and designing automated pipelines using Terraform.
- AI Infrastructure Integration: Partner directly with the development team to architect the infrastructure, secure data boundaries, and compliance guardrails required for active AI pipeline rollouts, Snowflake Cortex data layers, and Model Context Protocol (MCP) integrations.
- Enterprise Endpoint Engineering: Take a hands\-on leadership role in our active, ongoing Microsoft Intune rollout to ensure seamless corporate device management, deployment, and endpoint compliance.
- Security \& Compliance Governance: Act as the technical security advocate in project rooms, ensuring that data governance, audit trails, and proactive security controls are natively built into all cloud migrations.
What the Tech Stack \& Environment Looks Like
- Cloud Ecosystem: Nearly 100% Azure (App Services, Azure Virtual Desktop/VDI, M365, Identity Protection). A minimal on\-premises VMware remnant exists strictly for local domain controllers.
- Data Tier: Core data structures have migrated away from traditional SQL Server into Snowflake for enterprise analytics.
- Automation Gaps to Conquer: This role is a blank canvas for automation—Terraform, automated pipelines, and centralized scripting start with you.
Profile Requirements
- Proven Azure Deployment Experience: A verified professional background executing and scaling live, production\-grade Azure cloud services (not limited to lab environments or certifications alone).
- Cloud\-Native Default Mindset: Your immediate instinct to solve an infrastructure bottleneck is utilizing an Azure\-native PaaS framework rather than spinning up a new VM.
- Hands\-on Systems Automation: Strong proficiency writing production\-level scripts, with a baseline mastery of PowerShell (Python capabilities are highly valued).
- Regulated Industry Background: Experience navigating compliance\-driven frameworks where data governance, security controls, and strict audit trails are standard practice. A background in Financial Services, Banking, or Wealth Management is strongly preferred.
- Endpoint Management Expertise: Direct, practical experience working with Microsoft Intune for corporate endpoint compliance and deployment.
- Dynamic Communication: The ability to communicate complex cloud infrastructure concepts clearly and effectively, whether presenting in the C\-suite or collaborating in a technical developer standup.
- Culture Fit: A collaborative team member with a low\-ego, problem\-solving mindset who is eager to step up whenever the team needs help.
What This Role Is NOT
- This is not a mundane corporate patch\-and\-maintenance Windows VM role. You are being brought in to innovate, optimize, and rebuild architecture from the ground up.
- This is not a hyper\-siloed specialist track. Network infrastructure is light and stable; security is treated as a team\-wide mindset here, not an isolated silo.
- This is not a brutal hedge fund grind. We offer sustainable wealth management hours with an exceptional hybrid workplace balance.
Career Growth Path
Our CTO envisions this hire carrying immense systemic influence over where our global infrastructure grows. As our financial pillars converge and scale, the right engineer will find an aggressive upward trajectory into direct Infrastructure Leadership, Advanced Data Architecture, or a executive CISO track.
Pay: $200,000\.00 \- $230,000\.00 per year
Benefits:
- Flexible schedule
Work Location: Hybrid remote in New York, NY
Salary Context
This $200K-$230K range is above the median for AI/ML Engineer roles in our dataset (median: $181K across 1996 roles with salary data).
View full AI/ML Engineer salary data →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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Epicsoft Tech, 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 $178,940 based on 11,900 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($215K) sits 20% above the category median. Disclosed range: $200K to $230K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Epicsoft Tech AI Hiring
Epicsoft Tech has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $230K - $230K.
Location Context
AI roles in New York pay a median of $210,000 across 2,448 tracked positions. That's 5% above the national 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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