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About This Role
At Elara Caring, we have a unique opportunity to play a huge role in the growth of an entire home care industry. Here, each employee has the chance to make a real difference by carrying out our mission every day. Join our elite team of healthcare professionals, providing the Right Care, at the Right Time, in the Right Place.
Job Description:
At Elara Caring, we care where you are and believe the best place for your care is where you live. We know there’s no place like home, and that’s why our teams continue to provide high\-quality care to more than 60,000 patients each day in their preferred home setting. Wherever our patients call home and wherever they are on their journey of health, we care. Each team member has a part to play in this mission. This means you have countless ways to make a difference as a AI Automation Engineer II. Being a part of something this great starts by carrying out our mission every day through your true calling: developing an amazing team of compassionate and dedicated healthcare providers.
To continue to be an industry pioneer in delivering unparalleled care, we need a AI Automation Engineer II with commitment and compassion. Are you one of them? If so, apply today!
Why Join the Elara Caring mission?
- Work in a collaborative environment.
- Be rewarded with a unique opportunity to make a difference
- Competitive compensation package
- Tuition reimbursement for full\-time staff and continuing education opportunities for all employees at no cost
- Opportunities for advancement
- Comprehensive insurance plans for medical, dental, and vision benefits
- 401(K) with employer match
- Paid time off, paid holidays, family, and pet bereavement
- Pet insurance
As a AI Automation Engineer II you’ll contribute to our success in the following ways:
- Coordinate with stakeholders, vendors, and cross\-functional teams to ensure successful project delivery.
- Collaborate with business lines to identify and evaluate opportunities for Automating and optimizing business processes.
- Design, develop, deploy, and support AI automation solutions across the full lifecycle (build, test, deployment, monitoring, and optimization)
- Ensure AI automation solutions are reliable, scalable, and maintainable in production environments
- Stay up\-to\-date with industry trends and best practices in business process automation and systems integration.
- Will be available via MS Teams, email, and phone to provide timely answers to team members, vendors, and customers.
- Contribute to the ongoing development of the team by sharing knowledge, experience, and expertise with other team members.
- Capable of managing own workload with minimal supervision to meet tight deadlines.
- Innovates and streamlines processes, taking advantage of any automation opportunities, identifying future issues and plan accordingly.
- Measure and report on performance, impact, and effectiveness of automation solutions
- Adhere to organizational standards for data privacy, security, and responsible AI usage
What is Required?
- Bachelor’s degree in computer science, Information Systems, Software Engineering, or an equivalent combination of education and experience.
- 3\-6 years of experience in automation, integration, or software engineering roles, with 2\+ years focused on AI/ML solutions. Experience working in an Agile IT environment, required.
- Hands\-on experience with cloud\-based AI services particularly in the Azure Stack (e.g., Azure OpenAI, Azure AI Foundry).
- Building or contributing to agentic workflows, retrieval\-augmented generation (RAG), and LLM\-powered automation solutions
- Proficiency with integration and automation platforms (e.g., Logic Apps, Power Automate, Azure Functions, or equivalent)
- Familiarity with enterprise platforms such as ServiceNow and Microsoft Power Platform
- Working knowledge of Python for AI workflows, data processing, and integrations (preferred)
- Microsoft Certified: Azure AI Engineer Associate — preferred
- Microsoft Certified: Power Platform Developer Associate — preferred
- Microsoft or cloud AI certifications preferred.
- Other relevant AI/ML or cloud certifications — advantageous
- Experience building scalable, high quality agentic workflows, retrieval\-augmented generation (RAG) architectures, or LLM\-powered automation.
- Structures and analyzes data to support AI workflows, evaluate performance, and generate insights that improve solution design and outcomes.
- Resolves complex technical challenges by identifying root causes, evaluating options, and implementing effective solutions in dynamic environments.
- Analyzes workflows to identify automation opportunities and improves efficiency through optimized, AI\-enabled processes.
- Works effectively with cross\-functional teams (engineering, product, business stakeholders) to execute and deliver AI solutions and incorporate diverse inputs.
- Plans and prioritizes work to manage multiple tasks and support timely, consistent delivery of AI solutions.
- TRAVEL: 10% travel may be required for occasional internal meetings and business alignment
You will report to the AI Transformation Manager
*Equal Employment Opportunity**: We are proud to be an equal opportunity workplace and comply with state and federal affirmative action requirements. Individuals are recruited, hired, assigned and promoted without regard to race, national origin, religion, age, color, sex, sexual orientation, gender identity, disability, protected veteran status, or any other protected characteristic. If you require assistance due to a disability in the application or recruitment process, please submit a request via email at [email protected].*
*Pay \& Benefit Information**: Compensation for this role will be determined based on a variety of factors, including qualifications, skills, competencies, and relevant experience. Elara offers a broad range of benefits. Learn more at* *https://careers.elara.com/us/en/benefits*
*EVerify**: Elara Caring participates in E\-Verify after a job offer is accepted and Form I\-9 completed.*
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 Elara Caring, 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. Mid-level AI roles across all categories have a median of $194,400.
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.
Elara Caring AI Hiring
Elara Caring has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Dallas, TX, US.
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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