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About This Role
Summary
The AI Integration & Automation Developer designs, builds, tests, and supports AI-enabled integrations and automation solutions that improve the efficiency, scalability, and quality of Managed Services operations. This role is delivery-focused and hands-on, translating operational problems into practical automation and AI workflow solutions that reduce manual effort, improve service consistency, and create measurable business value.
This position requires strong critical thinking, curiosity about emerging AI automation capabilities, and the ability to evaluate where AI, orchestration, scripting, workflow automation, or traditional process improvement will drive the best outcome. The developer will work closely with technical and operational stakeholders and may receive dotted-line technical guidance from the AI Integration & Automation Engineer.
Essential Duties and Responsibilities
AI Integration & Automation Development
- Designs, develops, and maintains AI-enabled automations, integrations, and workflow solutions supporting Managed Services.
- Builds practical AI orchestration patterns that connect systems, data, prompts, APIs, scripts, and operational workflows.
- Develops automation solutions that reduce manual work, improve service delivery speed, increase consistency, or create measurable cost avoidance.
- Creates reusable components, connectors, scripts, and workflow patterns that can be leveraged across multiple use cases.
- Supports implementation of internal AI Orchestration capabilities and related automation services as part of the Managed Services automation portfolio.
Critical Thinking & Operational Problem Solving
- Analyzes operational pain points and determines whether AI, automation, integration, process improvement, or a combination of approaches is the best solution.
- Identifies automation opportunities that address systemic problems, not only isolated manual tasks.
- Balances experimentation with production stability, supportability, security, and operational readiness.
- Uses data, outcome measures, and stakeholder feedback to validate whether automation work is creating efficiency gains or business value.
AI Automation Delivery & Support
- Develops LLM-enabled workflows, agentic automation patterns, and AI-assisted operational processes with appropriate guardrails and fallback logic.
- Integrates with enterprise tools, monitoring platforms, ITSM systems, cloud services, and internal applications as needed.
- Troubleshoots automation defects, integration failures, and AI workflow issues, driving durable fixes and continuous improvement.
- Documents solution design, configuration, support steps, ownership, and knowledge articles to reduce key-person dependency.
- Participates in testing, UAT, release readiness, change control, and production support activities for automation solutions.
Managed Services, ITIL & Collaboration
- Applies awareness of Managed Services delivery models, ITIL processes, and operational impact when designing automation solutions.
- Collaborates with engineers, service owners, process owners, developers, and stakeholders to understand requirements and validate outcomes.
- Communicates technical concepts clearly to both technical and non-technical audiences.
- Stays current on emerging AI automation development practices, AI orchestration patterns, LLM tooling, integration frameworks, and service operations trends.
General
- Demonstrates and actively promotes an understanding and commitment to the mission of Logicalis through behaviors consistent with the organization's values.
- Maintains a working knowledge of applicable Federal, State, and Local laws and regulations as well as policies and procedures of Logicalis in order to ensure adherence in a manner that reflects honest, ethical, and professional behaviors.
- Supports and conducts self in a manner consistent with customer service expectations.
Supervisory Responsibilities
This job has no supervisory responsibilities. The role may receive dotted-line technical guidance from senior AI automation resources and may collaborate with other developers, engineers, and process owners.
Qualifications
To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
Education/Experience/Technical Requirements/Certifications
Equivalent combination accepted.
Education
- Bachelor’s degree in computer science, information technology, software engineering, or related field, or equivalent experience.
Experience / Technical Requirements
- 5+ years of experience developing automations, integrations, scripts, workflows, or application solutions in enterprise or services environments.
- Hands-on experience with automation development using tools or languages such as Python, PowerShell, JavaScript, APIs, workflow platforms, or low-code/no-code automation tools.
- Experience designing or supporting AI-enabled workflows, LLM-driven processes, AI agents, orchestration patterns, or intelligent automation solutions.
- Understanding of prompt design, structured outputs, human-in-the-loop patterns, guardrails, fallback logic, and practical considerations for production AI use cases.
- Experience integrating with operational platforms such as ITSM tools, monitoring/observability tools, cloud platforms, ticketing systems, knowledge bases, or internal business applications.
- Ability to analyze business or operational processes and convert requirements into working automation solutions.
- Working knowledge of Managed Services operations, ITIL practices, or service management processes is preferred.
- Experience with cloud environments such as Azure, AWS, or GCP is preferred.
- Experience with data pipelines, vector databases, embeddings, event-driven workflows, or containerized environments is a plus.
- Strong troubleshooting skills with the ability to identify root causes, evaluate trade-offs, and implement sustainable fixes.
- Strong communication and collaboration skills with the ability to work across technical and operational teams.
Certifications
- Certifications related to AI, automation, cloud platforms, ITIL, or service management are preferred but not required.
(Examples may include Microsoft Azure AI Engineer, Azure Administrator Associate, AWS certifications, ITIL Foundation, or relevant automation/platform certifications.)
Other Skills and Abilities
- Critical thinker with the ability to challenge assumptions, evaluate options, and recommend practical solutions.
- Curious and current on emerging AI automation development practices, orchestration frameworks, and service operations technology trends.
- Outcome-oriented mindset focused on measurable efficiency gains, cost avoidance, quality improvement, and operational scalability.
- Ability to operate as a hands-on developer while collaborating effectively with senior technical resources and business stakeholders.
- Strong documentation discipline, including solution design, support procedures, ownership details, release notes, and knowledge articles.
- Ability to manage multiple priorities in a fast-paced environment while maintaining quality and production stability.
- Comfortable experimenting, learning quickly, failing fast, and iterating toward durable solutions.
Physical Demands
The physical demands described here are representative of those that should be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
While performing the duties of this Job, the employee is constantly required to sit, talk, see, hear, and use hands and arms. The employee is frequently required to stand; move about, climb steps or balance and stoop, kneel, crouch, or crawl. The employee may occasionally lift and/or move up to 10 pounds.
The above statements describe the general nature and level of work being performed by individuals assigned to this classification. This is not intended to be an exhaustive list of all responsibilities and duties required of personnel so classified.
Logicalis is an Equal Opportunity Employer. It is our policy to employ people who are qualified by reason of education, training, experience, and demonstrated performance. We value inclusion and belonging at our company. We do not discriminate on the basis of race, color, religion, national origin, sexual orientation, gender identity and gender expression, marital status, age, height, weight, disability, veteran status, or any other reason prohibited by applicable federal or state laws.
Salary Compensation Range: $77,517 - $98,000/yr.
LogicalisUS Benefits Summary
Salary Context
This $77K-$98K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Logicalis, 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($87K) sits 59% below the category median. Disclosed range: $77K to $98K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Logicalis AI Hiring
Logicalis has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $98K - $98K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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