Interested in this AI/ML Engineer role at LDI CONNECT?
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About This Role
With headquarters on Long Island, come see why Long Island Business News honored LDI Connect with an award that recognizes our commitment to a high performing\- yet people\-centered workplace culture. Our other offices in CT, NYC (right in Time Square), NJ and LA share the same commitment!
LDI Connect is a high\-performing technology services company with a proven track record of creating rewarding careers.
We do it all \- from commercial security solutions, document management, managed IT services, hosted/cloud services, phone systems, and professional audio/video systems, AI and even copiers, printers, with a full line of production equipment.
LDI Connect is seeking an AI, Automation \& Security Engineer to support the implementation, administration, and continuous improvement of the organization's artificial intelligence, automation, and cybersecurity initiatives.
This role works closely with IT leadership to deploy secure AI solutions, automate business processes, strengthen cybersecurity controls, and ensure technology solutions align with organizational standards and compliance requirements.
The ideal candidate is passionate about emerging AI technologies, has experience working with major AI platforms, enjoys automating repetitive processes, and understands the importance of building secure, well\-governed solutions.
WHAT YOU’LL DO:* Deliver secure AI and automation solutions that improve business efficiency.
- Build and maintain reliable automation workflows for IT and business operations.
- Strengthen the organization's security posture through implementation of modern security controls.
- Become a trusted technical resource for AI, automation, and cybersecurity initiatives.
- Earn at least one advanced Microsoft certification in AI, Azure, or Security.
Artificial Intelligence* Evaluate, implement, and support AI\-powered business solutions using approved enterprise AI platforms.
- Develop AI\-assisted workflows that improve operational efficiency while protecting company data.
- Work with business stakeholders to identify practical AI use cases.
- Assist in developing AI governance standards, documentation, and usage guidelines.
- Test, evaluate, and compare AI models and services for business applications.
- Monitor AI implementations for performance, security, and compliance.
Automation* Design and maintain business workflows using Microsoft Power Platform, Azure Logic Apps, scripting, and APIs.
- Automate repetitive IT and business processes.
- Develop integrations between cloud services, SaaS applications, and internal systems.
- Document automation solutions and operational procedures.
Cybersecurity* Assist in implementing and maintaining security controls across Microsoft 365, Azure, and cloud applications.
- Participate in vulnerability management and remediation efforts.
- Monitor security alerts and assist with incident investigations.
- Support implementation of Zero Trust security principles.
- Assist with identity and access management, Conditional Access policies, and multifactor authentication.
- Participate in security assessments of AI and automation solutions.
- Contribute to compliance initiatives aligned with frameworks such as NIST Cybersecurity Framework and CIS Controls.
Cloud \& Infrastructure* Support Microsoft Azure and Microsoft 365 administration.
- Assist with cloud networking, identity integration, and API security.
- Configure and maintain cloud services supporting AI and automation initiatives.
- Collaborate with infrastructure teams on secure solution deployment.
Documentation \& Collaboration* Create technical documentation and solution diagrams.
- Maintain AI, automation, and security standards.
- Work with business departments to understand operational requirements.
- Provide technical guidance and end\-user support for AI and automation tools.
WHAT WE’RE LOOKING FOR:
Required Experience* Bachelor's degree in Information Technology, Cybersecurity, Computer Science, or related field (or equivalent experience).
- 1–5 years of experience in IT infrastructure, cloud administration, cybersecurity, automation, or software development.
- Experience administering Microsoft 365 and Microsoft Azure environments.
- Experience implementing workflow automation or scripting solutions.
Preferred Certifications \- One or more of the following is desirable:* Microsoft Azure Fundamentals (AZ\-900\)
- Microsoft Azure Administrator (AZ\-104\)
- Microsoft AI Fundamentals (AI\-900\)
- Microsoft Security, Compliance, and Identity Fundamentals (SC\-900\)
- Microsoft Security Operations Analyst (SC\-200\)
- CompTIA Security\+
- CompTIA CySA\+
- ISC2 Certified in Cybersecurity (CC)
Desired Personal Attributes* Passion for learning emerging AI technologies.
- Strong analytical and troubleshooting skills.
- Security\-first mindset.
- Ability to communicate technical concepts to non\-technical users.
- Self\-motivated with strong organizational skills.
- Curious, adaptable, and eager to explore new technologies.
- Ability to manage multiple projects in a fast\-paced environment.
Preferred Technical Skills
*AI Platforms \-* Experience with one or more enterprise AI platforms, such as:* Microsoft Copilot
- Azure OpenAI
- ChatGPT
- Claude
- Google Gemini
- Amazon Bedrock
*Experience may include prompt engineering, API integration, automation, model evaluation, or business process implementation.*
*Automation** Microsoft Power Automate
- Azure Logic Apps
- PowerShell
- Python
- REST APIs
- JSON
- SQL
*Security** Microsoft Entra ID
- Microsoft Defender
- Microsoft Intune
- Conditional Access
- Identity and Access Management (IAM)
- Multi\-Factor Authentication (MFA)
- Microsoft Sentinel (preferred)
- Basic networking and cloud security concepts
*Cloud** Microsoft Azure
- Azure App Services
- Azure Storage
- Azure Functions
- Azure API Management
- Microsoft Graph API
The typical base salary for this role is between $70,000 and $80,000\. The salary offered will depend on experience.
*LDI Connect and affiliates provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.*
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Salary Context
This $70K-$80K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At LDI CONNECT, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($75K) sits 66% below the category median. Disclosed range: $70K to $80K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
LDI CONNECT AI Hiring
LDI CONNECT has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Jericho, NY, US. Compensation range: $80K - $80K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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