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About This Role
About us
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KnitWell Group, a specialty retail operating company, comprises some of the most iconic brands in America – Ann Taylor, Chico's, Haven Well Within, Lane Bryant, LOFT, Soma, Talbots, White House Black Market. Individually, our brands are unique and strong. Together, we are powerful.
Our common thread is our commitment to create exceptional products, provide memorable experiences, and achieve superior results. Our associates are innovators who thrive through collaboration and are dedicated to excellence. At the heart of it all are the customers we serve. We are dedicated to creating fashion that not only looks good but also makes our customers feel good. About the role
Reporting to the Vice President of Artificial Intelligence, The Director, AI Solutions Delivery will lead the technical execution of AI initiatives, ensuring successful deployment, integration, and scaling of AI solutions across the enterprise. This role requires a blend of technical expertise, project delivery experience, and cross\-functional leadership.
You will be responsible for transitioning POCs \& pilots into scalable production solutions and ensuring technical readiness across platforms and teams. You will also support pilots where deeper technical involvement is needed, acting as a thought partner and solution architect.
The impact you can have
- Operate as an independent partner to business leaders, proactively identifying opportunities, shaping AI use cases, and translating business needs into actionable technical solutions without requiring heavy direction.
- Build trusted relationships across business functions, guiding stakeholders from idea through implementation while ensuring solutions are aligned to strategic goals, operational realities, and adoption needs.
- Lead business\-facing solution delivery with a high degree of autonomy, making decisions, driving clarity, and moving initiatives forward across cross\-functional teams.
- Own the end\-to\-end delivery of AI solutions from pilot/PoC to full production deployment across business functions.
- Create detailed technical plans, timelines, and resource allocation to ensure successful implementation of AI solutions.
- Partner with IT and data teams to ensure AI solutions integrate with existing systems and data infrastructure.
- Evaluate and manage third\-party AI tools, platforms, and external development partners.
- Ensure best practices around data governance, model monitoring, and performance evaluation are followed during and after deployment.
- Identify opportunities to improve delivery processes and implement tools for versioning, testing, and performance tracking.
- Potential on\-call work for launched AI projects
You’ll bring to the role
- 7\+ years in technical delivery, AI/ML engineering, data engineering, or solution architecture, with at least 2 years managing production AI or data science projects.
- Familiarity with machine learning models, data pipelines, cloud platforms (AWS, Azure, or GCP), and MLOps practices.
- Experience leading cross\-functional teams through the full solution delivery lifecycle.
- Ability to communicate complex technical topics to business stakeholders and align delivery plans with organizational needs.
- Bachelor’s degree in Computer Science, Engineering, or related field
- Master’s degree or certifications in AI/ML a plus.
- Technically fluent but can zoom out to see the bigger business picture.
- Obsessed with delivery excellence and operational scalability.
- A proactive problem solver who anticipates risks and removes roadblocks.
- Adept at leading through influence, not just authority.
Benefits
- You will be eligible to receive a merchandise discount at select KnitWell Group brands, subject to each brand’s discount policies.
- Support for your individual development plus opportunities for career mobility within our family of brands.
- A culture of giving back – local volunteer opportunities, annual donation and volunteer match to eligible nonprofit organizations, and philanthropic activities to support our communities.\*
- Medical, dental, vision insurance \& 401(K).\*
- Employee Assistance Program (EAP).
- Time off – paid time off \& holidays.\*
- Any job offer will consider factors such your qualifications, relevant experience, and skills. Eligibility of certain benefits and associate programs are subject to employment type and role.
Remote: Preference will be given to candidates in Columbus, OH, Hingham, MA or Ft. Myers, FL areas near one of our corporate offices. This position is also open to remote candidates. Occasional travel to a company office may be required.
Applicants to this position must be authorized to work for any employer in the US without sponsorship. We are not providing sponsorship for this position.
\#LI\-AP1
Location:
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Ascena Corp ETNA\-ascena\-Pataskala, OH 43062Position Type:
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Regular/Full time Equal Employment Opportunity
The Company is an equal opportunity employer and welcomes applications from diverse candidates. Hiring decisions are based upon a candidate's qualifications as they relate to the requirements of the position under consideration and are made without regard to sex (including pregnancy), race, color, national origin, religion, age, disability, genetic information, military status, sexual orientation, gender identity, or any other category protected by applicable law. The Company is committed to providing reasonable accommodations for job applicants with disabilities. If you require an accommodation to perform the essential duties of the position you are seeking or to participate in the application process, please contact [email protected]. The Company will make reasonable accommodations for otherwise qualified applicants or employees, unless such accommodations would impose an undue hardship on the operations of the Company’s business. The Company will not revoke or alter a job offer based on an applicant’s request for reasonable accommodation.
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 KnitWell Group, 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. Director-level AI roles across all categories have a median of $274,554.
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.
KnitWell Group AI Hiring
KnitWell Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Pataskala, OH, 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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