Interested in this AI/ML Engineer role at LogicMonitor?
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About Us:
We love going to work and think you should too. Our team is dedicated to trust, customer obsession, agility, and striving to be better everyday. These values serve as the foundation of our culture, guiding our actions and driving us towards excellence. We foster a culture of performance and recognition, allowing us to transform growth as we enable our employees to do the best work of their careers.
This role is open to candidates based in or near Austin, TX. Our Austin office is based in the vibrant San Jacinto Center downtown with breathtaking views of Lady Bird Lake. At LogicMonitor, we hire within our Centers of Energy—vibrant locations where our teams connect, collaborate, and innovate.
To learn more about life at LogicMonitor, check out our Careers Page.
What You'll Do:
LogicMonitor® is the AI\-first hybrid observability platform powering the next generation of digital infrastructure. LogicMonitor delivers complete visibility and actionable intelligence across on\-premises, cloud, and edge environments. By anticipating issues before they strike, optimizing resources in real time, and enabling faster, smarter decisions, LogicMonitor helps IT and business leaders protect margins, accelerate innovation, and deliver exceptional digital experiences without compromise.
Our customers love LogicMonitor's ability to bring cloud and traditional IT together into one view, as seen in minimal churn rates, expansion business, and exciting new customer references. In fact, LogicMonitor has received the highest Net Promoter Score of any IT Infrastructure Management provider. LogicMonitor also boasts high employee satisfaction. We have been certified as a Great Place To Work®, and named one of BuiltIn's Best Places to Work for the seventh year in a row!
The Lead Solutions Architect, Edwin owns complex and strategic Edwin implementations and operating\-model handoffs within the AI Delivery motion, especially where Edwin runs in parallel with Envision, third\-party data sources, ITSM workflows, FDE support, or partner delivery.
This role acts as a senior technical advisor, regional or domain subject\-matter expert, mentor, and cross\-functional delivery leader. The Lead Solutions Architect handles complex customer environments, advanced ITSM workflows, multi\-platform integrations, pre\-sales validation, repeatable delivery patterns, Project transition risks, and structured feedback to Product and Engineering.
This role is not simply "more implementations." The Lead Solutions Architect is expected to reduce delivery risk, improve time\-to\-value, mentor others, build repeatable practices, and help scale delivery beyond individual heroics. That aligns with the transcript discussion around senior roles focusing on strategic work, complex accounts, enablement, regional expertise, and process\-building rather than repetitive project churn.
Here's a closer look at this key role:
Complex and Strategic Implementation Delivery
- Lead complex or strategic Edwin implementations for enterprise and high\-value customers.
- Design advanced Edwin solutions that coordinate with Envision, core PS/PM ownership, ITSM workflows, third\-party observability sources, FDE support, and partner\-delivered work.
- Own architecture for complex event ingestion, alert correlation, enrichment, investigation, incident creation, ticket routing, escalation, remediation, and value reporting.
- Support major customers where continuity, regional context, Project transition planning, or advanced technical judgment is required.
- Ensure complex implementations drive measurable time\-to\-value while protecting core PS capacity and customer continuity.
Advanced ITSM and ITOps Workflow Design
- Design advanced workflows across ITSM platforms such as:
- ServiceNow
- Freshservice
- ConnectWise
- Jira Service Management
- Zendesk
- Similar enterprise service management tools
- Translate complex customer operating models into scalable ITOps workflows.
- Define how tasks, events, alerts, incidents, tickets, changes, escalations, and remediation actions should flow across systems.
- Advise customers on where to automate, where to enrich, where to route, where to escalate, and where human review should remain.
- Ensure workflow designs reflect how the customer actually operates, not just what the tools technically allow.
Multi\-Platform Integration Leadership
- Design and validate integrations between LogicMonitor products and third\-party platforms such as:
- Dynatrace
- SolarWinds
- ThousandEyes
- Splunk
- Datadog
- New Relic
- AppDynamics
- Other customer\-specific monitoring, observability, APM, ITOM, or analytics platforms
- Build repeatable integration patterns for common third\-party platforms.
- Troubleshoot advanced integration issues involving payload transformation, event normalization, deduplication, correlation, field mapping, routing logic, and bidirectional updates.
- Identify where customer requirements expose product gaps, delivery limitations, or opportunities for reusable patterns.
Pre\-Sales Validation and Solution Design
- Support strategic pre\-sales opportunities where Edwin, Envision handoffs, ITSM workflows, third\-party integrations, FDE support, partner delivery, or delivery feasibility require validation.
- Partner with AEs and SEs to clarify implementation scope, technical feasibility, risk, value outcomes, and realistic customer expectations.
- Identify gaps between what is being promised and what can be delivered.
- Help define customer use cases before implementation begins.
- Build early technical alignment with customers to improve post\-sale delivery success.
Regional or Domain SME Ownership
- Serve as a primary Edwin subject\-matter expert for a region, market, or specialized domain.
- Support regional PS, SE, Support, Customer Success, and partner teams with advanced technical questions.
- Represent regional delivery realities and customer requirements back to PS leadership, Product, and Engineering.
- Help determine which work belongs with AI Delivery, core PS/PM, CS, partners, Product, Engineering, or FDE teams.
Mentoring and Enablement
- Mentor Solutions Architects on Edwin delivery patterns, Envision handoff points, ITSM integrations, third\-party integrations, customer\-coverage handoffs, and ITOps workflow design.
- Create reusable templates, architecture patterns, workflow diagrams, troubleshooting guides, and implementation playbooks.
- Lead enablement sessions for PS, SEs, Support, regional teams, and partners.
- Help reduce dependency on a small number of experts by turning implementation experience into repeatable delivery practices.
Product and Engineering Collaboration
- Provide structured feedback to Product and Engineering based on real implementation experience.
- Document product gaps, enhancement requests, integration limitations, roadmap needs, and field risks.
- Help distinguish between one\-off customer customization, repeatable product need, and delivery process gap.
- Participate in escalation, roadmap, and readiness discussions where field experience is needed.
What You'll Need:
- 6\+ years experience in Solutions Architecture, Professional Services, implementation consulting, enterprise SaaS delivery, technical architecture, or advanced customer engineering.
- Strong working knowledge of service delivery, observibility/monitoring handoff points, data flows, ITSM workflows, third\-party observability sources, and the broader customer operations ecosystem.
- Deep working experience with ITSM platforms such as ServiceNow, Freshservice, and ConnectWise.
- Experience designing integrations across multiple monitoring, observability, APM, ITOM, analytics, and workflow platforms.
- Advanced ability to translate tasks, events, alerts, incidents, tickets, changes, escalations, and remediation steps into ITOps workflows.
- Strong understanding of event correlation, alert noise reduction, ticket enrichment, routing, escalation, incident lifecycle design, and operational handoffs.
- Experience leading complex customer design sessions.
- Ability to mentor technical peers.
- Ability to work cross\-functionally with Sales, SEs, PS, Product, Engineering, Support, Customer Success, and partners.
- Strong written and verbal communication skills.
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*Anticipated Application Close Date: 09/31/26*
*LogicMonitor is an Equal Opportunity Employer*
*At LogicMonitor, we believe that innovation thrives when every voice is heard and each individual is empowered to bring their unique perspective. We're committed to creating a workplace where diversity is celebrated, and all employees feel inspired and supported to contribute their best.*
*For us, equal opportunity means fostering a truly inclusive culture where everyone has the chance to grow and succeed. We don't just open doors; we invite you to step through and be part of something bigger. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.*
*Work Authorization:*
*At this time, we are able to consider candidates who are authorized to work in the United States on a full\-time, permanent basis without requiring new or initial employer\-sponsored work authorization.*
*Candidates who currently hold valid U.S. work authorization that can be transferred to a new employer (such as certain H\-1B statuses) may be considered on a case\-by\-case basis.*
*We are not able to provide new sponsorship for employment\-based visas that require an initial petition or application by the employer.*
###### \#LI\-DW1 \#LI\-Hybrid \#BI\-Hybrid
Our goal is to ensure an accessible and inclusive experience for every candidate.
If you need a reasonable accommodation during the application or interview process under applicable local law, please submit a request via this Accommodation Request Form.
Know your rights: workplace discrimination is illegal. Please click here to review LogicMonitor's U.S. Pay Transparency Nondiscrimination Provision.
Salary Context
This $136K-$189K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At LogicMonitor, 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 in Demand for This Role
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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($162K) sits 24% below the category median. Disclosed range: $136K to $189K.
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.
LogicMonitor AI Hiring
LogicMonitor has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Austin, TX, US. Compensation range: $189K - $189K.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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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