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About This Role
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Job Summary:
JCS Solutions LLC is seeking a Senior AIOps Engineer to support critical mission operations within a secure environment and lead the transformation of our IT Service Management (ITSM) capabilities. This role is responsible for the design, deployment, and management of AIOps solutions that enhance the reliability and security of Department of War (DoW) networks and systems.
Acting as the technical lead for this initiative, you will orchestrate integrations across existing Network Engineering, ServiceNow, and SolarWinds teams. You will utilize Splunk and the Machine Learning Toolkit (MLTK) to provide descriptive and predictive analytics and establish closed\-loop automated incident response, ensuring the high availability of mission\-essential infrastructure.
What’s in it for you:
- Join a premier technology firm specializing in innovative solutions.
- Be part of a collaborative, inclusive, and innovative work culture.
- Enjoy tremendous growth potential in a high\-performing team environment.
- A robust benefits package:
+ Health, dental, and vision insurance
+ Life insurance
+ Short\-and\-long term disability
+ Paid time off (PTO)
+ 401k retirement plan with employer match
+ Annual Professional Development Reimbursement Program
+ And more!
What you will do:* Cross\-Functional Leadership: Lead the AIOps platform initiative by acting as the primary technical liaison to existing Network Engineering, ServiceNow, and SolarWinds administration teams to establish unified telemetry pipelines.
- ITSM Orchestration \& Automation: Architect closed\-loop remediation workflows by deeply integrating Splunk ITSI alerts with ServiceNow Event Management and Incident Management modules.
- Mission\-Critical Observability: Architect and maintain Splunk AIOps solutions across unclassified and classified enclaves to provide real\-time situational awareness.
- Infrastructure Telemetry Integration: Normalize and correlate network performance and fault data from SolarWinds with server and application logs to provide a holistic view of enterprise health.
- Advanced ML Development: Deploy custom machine learning models via Splunk MLTK to identify anomalous behavior, potential cyber threats, and infrastructure degradations.
- Secure Data Integration: Engineer secure data ingestion pipelines for telemetry data from cross\-domain solutions and tactical edge devices.
- Incident Reduction: Utilize IT Service Intelligence (ITSI) to correlate multi\-source events, reducing noise and prioritizing high\-impact mission alerts.
- Cyber Defense Support: Collaborate with the Cyber Security Service Provider (CSSP) to integrate AIOps insights into defensive cyber operations (DCO).
- Compliance \& Documentation: Ensure all observability tools comply with DoW STIGs and IL5/IL6 protocols; develop and maintain architectural documentation and compliance traceability.
- Mission Alignment: Stay current on AIOps and related capabilities relevant to DoD, federal, and intelligence mission systems.
What you will bring:* Security Clearance: Active Top Secret / Sensitive Compartmented Information (TS/SCI) required at time of hire.
- Certification: Active IAT Level II certification (e.g., Security\+ CE, CySA\+, GSEC, or SSCP) required.
- Citizenship: United States Citizenship is required.
- Platform Experience: 7\+ years of experience with Splunk Enterprise, including architectural design, cluster management, and advanced Search Processing Language (SPL).
- AIOps \& ITSM: 3\+ years of experience implementing AIOps workflows, including integration with enterprise ITSM solutions (ServiceNow) for automated root cause analysis and remediation.
- Machine Learning: Proven track record of building, testing, and tuning supervised and unsupervised models within the Splunk MLTK.
- Scripting \& Automation: Advanced scripting skills for developing custom search commands, API integrations, and automating remediation tasks (e.g., Python).
- Leadership: Experience leading technical working groups and directing the efforts of adjacent infrastructure and development teams.
- Operational Experience: Prior experience working within a DoW/DoD Operations Center (NOC/SOC) or supporting mission\-critical systems and networks.
- Communication: Must be able to present designs, plans, and analyses of alternatives to technical leadership boards for approvals.
How you will wow us:* Enterprise Aggregation: Experience aggregating and correlating telemetry from diverse tools, specifically SolarWinds, ServiceNow, and VMware vCenter.
- Expert Certification: Splunk Enterprise Certified Architect or Splunk ITSI Certified Admin.
- Cloud Observability: Experience with Cloud Native Computing Foundation (CNCF) observability tools in secure hybrid multi\-cloud environments (Azure/AWS).
- RMF/ATO Knowledge: Understanding of the Risk Management Framework (RMF) and the Authorization to Operate (ATO) process for AI/ML workloads.
JCS Solutions (JCS) is a premier technology firm providing innovative solutions and high\-quality services in defense, national security, and civilian sectors. JCS offers enterprise\-wide solutions including cloud computing, software development, cybersecurity, digital modernization, and management consulting for the federal government. At JCS, we elevate our customers’ mission through the application of technology and professional services. Our commitment to investing in our workforce drives innovation and progress for our clients, employees, and communities.
JCS is both a Great Place to Work and a Top Places to Work certified company.
Our employees embody our core values, and we are looking for others who do too!* Customer Experience: Strive for excellence and delight our clients
- Innovation: Embrace creative thinking to enable continual growth and powerful solutions
- Accountability: Take ownership of and pride in our actions and service delivery
- Inspire: Be inspired to be your best self and have fun in the process
- Integrity: Do the right thing, the right way, every time!
- Stewardship: The careful and responsible management of something entrusted to our care.
At JCS Solutions, compensation is based on a number of factors such as location, qualifications, and applicable contract terms. The general salary range for this position is as follows: $103,000\.00 \- $155,000\.00\.
Commitment to non\-discrimination: All qualified applicants will receive consideration for employment without regard to any status protected by applicable federal, state, or local laws. NOTICE: *Please be aware that all JCS Solutions communications related to job interviews and offers from our recruiting team will only come from @JCSSolutions.com. We want to emphasize that we do not conduct any interviews over Discord, Slack, Skype, Zoom, or any chat app.*
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Salary Context
This $103K-$155K range is in the lower quartile 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 JCSSolutions, 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($129K) sits 40% below the category median. Disclosed range: $103K to $155K.
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.
JCSSolutions AI Hiring
JCSSolutions has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Albuquerque, NM, US. Compensation range: $155K - $155K.
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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