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Remote Reliability Manager Jobs in Waco, TX (NOW HIRING)

... remote within a mutually acceptable location. #LI-Hybrid Success Looks Like: * AI systems move ... Ensure AI systems follow best practices for reliability, observability, and cost management.

Remote Position Type: Full-Time Position Summary We are seeking an experienced sales leader who is ... Discovery, qualification, pipeline management, forecasting, follow-up, and closing. You'll train ...

New

Remote Position Type: Full-Time Position Summary We are seeking an experienced sales leader who is ... Discovery, qualification, pipeline management, forecasting, follow-up, and closing. You'll train ...

New

Remote Position Type: Full-Time Position Summary We are seeking an experienced sales leader who is ... Discovery, qualification, pipeline management, forecasting, follow-up, and closing. You\'ll train ...

Remote Position Type: Full-Time Position Summary We are seeking an experienced sales leader who is ... Discovery, qualification, pipeline management, forecasting, follow-up, and closing. You'll train ...

New

Remote Reliability Manager information

See Waco, TX salary details

$55K

$104.3K

$149.6K

How much do remote reliability manager jobs pay per year?

As of Jul 31, 2026, the average yearly pay for remote reliability manager in Waco, TX is $104,300.00, according to ZipRecruiter salary data. Most workers in this role earn between $83,900.00 and $124,300.00 per year, depending on experience, location, and employer.

What is the difference between Remote Reliability Manager vs Remote Maintenance Engineer?

AspectRemote Reliability ManagerRemote Maintenance Engineer
CredentialsEngineering degree, certifications in reliability or asset managementEngineering degree, certifications in maintenance or technical skills
Work EnvironmentOversees reliability strategies remotely, collaborates with teamsPerforms maintenance tasks remotely or on-site, technical troubleshooting
Industry UsageUsed across manufacturing, energy, and industrial sectorsCommon in manufacturing, utilities, and industrial facilities
Search IntentComparing reliability management roles with maintenance rolesLooking for maintenance-focused remote engineering jobs

The Remote Reliability Manager focuses on developing and implementing strategies to improve asset reliability remotely, often overseeing teams and analyzing data. In contrast, the Remote Maintenance Engineer handles technical maintenance tasks, troubleshooting, and repairs remotely or on-site. Both roles require engineering credentials and are prevalent in industrial sectors, but their core responsibilities differ—one emphasizes strategic reliability management, the other technical maintenance execution.

What job categories do people searching Remote Reliability Manager jobs in Waco, TX look for? The top searched job categories for Remote Reliability Manager jobs in Waco, TX are:
What cities near Waco, TX are hiring for Remote Reliability Manager jobs? Cities near Waco, TX with the most Remote Reliability Manager job openings:

AI Technical Lead

Blue Cross of Idaho

Meridian, TX • On-site, Remote

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 20 days ago


Blue Cross of Idaho rating

6.0

Company rating: 6.0 out of 10

Based on 12 frontline employees who took The Breakroom Quiz

278th of 300 rated insurance


Job description

Our AI Technical Lead is responsible for designing and delivering scalable AI systems that enable intelligent applications across the organization. This role combines hands-on engineering, system architecture, and technical leadership to build production-grade machine learning and generative AI platforms. The Lead works closely with product, data engineering, and infrastructure teams to bring AI capabilities from experimentation into reliable production systems while supporting the organization's broader AI strategy and innovation initiatives.

Location: this position has preference to based in hybrid work location (onsite and WFH). There may be opportunity for fully remote within a mutually acceptable location. #LI-Hybrid

Success Looks Like:

  • AI systems move efficiently from experimentation and pilot phases into reliable production environments.

  • Engineering teams operate within clear architectural standards and scalable development practices.

  • AI capabilities deliver measurable business impact.

  • The organization is able to rapidly develop, test, and scale new AI-driven solutions

Key Responsibilities:

Technical Leadership

  • Provide technical leadership and mentorship to a team of AI engineers.

  • Establish engineering standards, coding practices, and architectural guidelines for AI system development.

  • Lead design reviews, guide technical decision making, and resolve complex engineering challenges.

  • Serve as a technical escalation point for AI system architecture and implementation

AI System Architecture

  • Architect end-to-end AI systems including data pipelines, model training workflows, AI service layers, and scalable AI application infrastructure.

  • Design and implement AI-powered applications including large language model (LLM) systems and retrieval-based knowledge applications.

  • Define architecture patterns that support experimentation, rapid prototyping, and production deployment of AI capabilities.

  • Develop service-based architectures that enable AI functionality to be integrated across enterprise applications.

AI Engineering & Development

  • Develop and deploy machine learning and generative AI solutions that support enterprise use cases.

  • Build reusable AI services and platform components that enable teams to rapidly develop and scale AI capabilities.

  • Implement evaluation, monitoring, and reliability systems to ensure consistent model performance.

  • Optimize AI pipelines for performance, scalability, and operational efficiency.

Cloud & MLOps

  • Design cloud-native infrastructure supporting AI and machine learning workloads.

  • Implement containerized AI services and automated deployment pipelines.

  • Support the development of scalable AI platforms that enable experimentation, model deployment, and operational monitoring.

  • Ensure AI systems follow best practices for reliability, observability, and cost management.

Collaboration & Delivery

  • Work closely with product managers, data engineers, and business stakeholders to identify and deliver high-value AI use cases.

  • Translate business requirements into scalable AI architecture and engineering solutions.

  • Partner with cross-functional teams to move AI solutions from pilots and experimentation into production environments.

  • Support initiatives that enable the organization to scale AI capabilities across multiple business domains.

Responsible AI & Governance

  • Promote responsible AI practices including transparency, fairness, and privacy considerations.

  • Implement safeguards and monitoring systems for AI applications operating in production.

  • Collaborate with security and compliance teams to ensure AI systems meet regulatory and organizational standards.

Required Education (must meet one of the following):

  • Bachelor or International Equivalency degree in Cybersecurity, Computer Science, Electrical Engineering, Information Systems, or closely related field of study; or equivalent work experience (Two years' relevant work experience is equivalent to one-year college)

  • Associate Degree in Computer Science, Electrical Engineering, Information Systems, or closely related field of study + 2 years additional experience

Required Experience: 6/+ years of experience in software engineering, machine learning engineering, and/or related AI/ML technical roles. Experience should include:

  • Experience designing and deploying machine learning or generative AI systems.

  • Strong programming experience in Python and modern backend technologies.

  • Experience building distributed systems or cloud-native architectures.

  • Experience implementing machine learning workflows or model deployment pipelines.

Preference for additional experience in:

  • Developing large language model (LLM) applications.

  • Experience with retrieval-based AI systems or knowledge-driven applications.

  • Working with cloud platforms and modern DevOps practices.

  • Mentoring engineers, leading technical initiatives, and/or serving as a technical lead

  • Working with large-scale data pipelines

As of the date of this posting, a good faith estimate of the current pay range is $118,506 - $177,758. The position is eligible for an annual incentive bonus (variable depending on company and employee performance). The pay range for this position takes into account a wide range of factors including, but not limited to, specific competencies, relevant education, qualifications, certifications, relevant experience, skills, seniority, performance, travel requirements, internal equity, business or organizational needs, and alignment with market data. At Blue Cross of Idaho, it is not typical for an individual to be hired at or near the top range for the position. Compensation decisions are dependent on factors and circumstances at the time of offer.

We offer a robust package of benefits including paid time off, paid holidays, community service and self-care days, medical/dental/vision/pharmacy insurance, 401(k) matching and non-contributory plan, life insurance, short and long term disability, education reimbursement, employee assistance plan (EAP), adoption assistance program and paid family leave program.

We will adhere to all relevant state and local laws concerning employee leave benefits, in line with our plans and policies.

Reasonable accommodations

To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed above 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.

We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, national origin, religion, sexual orientation, gender identity, status as a veteran, and basis of disability or any other federal, state or local protected class.


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