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About This Role
Job Description
Job Title: Director, AI Center of Excellence
Department: Technology
Reports To: Chief Technology Officer
Classification: Exempt
Management Level: Director
Position Summary
The Director, AI Center of Excellence will build and lead the AI Center of Excellence for LanguageLine, while also supporting broader Teleperformance Specialized Services collaboration where shared opportunities, standards, and reuse make sense. This leader will help shape and turn AI strategy into a practical operating model by establishing governance, prioritizing opportunities, enabling responsible adoption, and helping business teams convert AI ideas into measurable operational and client value.
This role reports to the CTO and serves as the day\-to\-day leader of the COE. The Director will balance strategy, execution, governance, and stakeholder leadership with a primary focus on LanguageLine and a secondary focus on cross\-company coordination across Specialized Services where appropriate.
Key Essential Functions:
Lead the AI Center of Excellence
- Stand up and lead the operating model for the AI Center of Excellence
- Help shape executive thinking on AI priorities and translate that direction into a practical AI roadmap, delivery model, and governance framework
- Build the COE as a lean, high\-credibility enablement function rather than a centralized bottleneck
Drive AI Strategy and Portfolio Prioritization
- Partner with LanguageLine business and functional leaders to identify, assess, and prioritize AI opportunities, with secondary support for broader Specialized Services coordination where relevant
- Build and maintain a LanguageLine\-focused AI roadmap aligned to business goals, risk posture, and enterprise priorities, while helping identify shared opportunities across Specialized Services
- Create clear intake, prioritization, and decision frameworks for AI initiatives
- Regularly present AI COE progress, priorities, key developments, risks, and market\-relevant AI updates to the LanguageLine executive team
Establish Responsible AI Governance
- Define and operationalize AI governance, policies, standards, and playbooks aligned with TP Global AI Policy and ISO 42001
- Develop practical risk\-tiering and review approaches for AI initiatives
- Ensure the COE supports responsible model development, usage, procurement, and deployment without creating unnecessary friction
Accelerate Adoption and Business Value
- Partner with domain teams on high\-impact, complex, or high\-risk AI initiatives
- Support business case development, benefits framing, and implementation planning
- Create mechanisms to measure AI adoption, effectiveness, and business impact
- Work with business functions across Specialized Services, including sales, finance, marketing, HR, operations, and other teams, to identify AI enablement opportunities and role\-specific capability gaps
- Work closely with LanguageLine functions, including sales, finance, marketing, HR, operations, and other teams, to identify AI enablement opportunities and role\-specific capability gaps, and extend that coordination to Specialized Services businesses where useful
Build Reuse and Organizational Capability
- Establish and maintain a living inventory of AI systems, experiments, and use cases across Specialized Services
- Create systems to capture and share AI learnings, tools, models, and implementation patterns
- Lead training, roundtables, and enablement efforts that improve AI literacy and readiness across the organization
- Assess AI training needs across functions and help define role\-based learning paths for leaders, managers, builders, and frontline or operational teams
- Deliver selected training and awareness sessions directly while also coordinating with HR, learning teams, and external partners where scaled or specialized training is needed
- Partner with HR and learning teams to operationalize required AI training, awareness, and policy rollout through enterprise mechanisms such as Workday where appropriate
Lead a Small Applied AI Team
- Hire, coach, and lead a small team supporting rapid prototyping, enablement, and delivery acceleration
- Oversee a lightweight SWAT capability for AI proofs of concept and early\-stage pilots
- Set a high bar for pragmatism, speed, responsible AI practices, and business alignment
What Success Looks Like
Within the first year, this leader should be able to:
- establish the COE as a trusted capability within LanguageLine, with credible extension points into broader Specialized Services collaboration
- establish a strong, credible reporting cadence with the LanguageLine executive team
- deliver a credible inventory of AI systems, opportunities, and risks
- implement a practical AI governance model aligned with enterprise policy
- launch a prioritized roadmap of high\-value AI opportunities
- improve reuse of AI tools, lessons, and patterns across companies
- establish a practical AI literacy and training approach across key functions, with HR\-supported rollout for required learning where needed
- demonstrate measurable wins in adoption, efficiency, client value, or workforce augmentation
Candidate Profile
The right candidate is comfortable operating across strategy, transformation, governance, and execution. This is not a pure research role and not a pure PMO role. It requires someone who can work credibly with executives, business leaders, technologists, and hands\-on builders.
Preferred Experience
- Experience leading or scaling an AI, automation, digital transformation, or innovation function
- Experience defining governance, operating models, and delivery mechanisms for emerging technology initiatives
- Experience partnering with business leaders to prioritize and operationalize AI use cases
- Experience working across multiple business functions, such as sales, finance, marketing, HR, and operations, to identify adoption barriers, workflow opportunities, and training needs
- Experience working with modern AI capabilities, including LLM\-based workflows, copilots, automation, and model\-enabled business applications
- Practical experience using AI\-assisted development and coding tools to improve team velocity, prototyping, and software delivery workflows. Examples may include tools such as Codex, Claude, GitHub Copilot, Cursor, or similar platforms.
- Experience balancing experimentation speed with security, risk, legal, and compliance considerations
- Experience designing, leading, or scaling technology adoption, enablement, or training programs is strongly preferred
- Experience building cross\-functional programs across multiple business units or companies
- Experience leading small high\-performing teams in ambiguous environments
Preferred Knowledge, Skills \& Abilities
- Strong executive communication and stakeholder management
- Executive presence and the ability to brief senior leadership on AI progress, implications, opportunities, and risks in clear business terms
- Ability to translate between technical teams and business leaders
- Strong judgment in prioritization, governance, and organizational design
- Practical understanding of responsible AI, model risk, and AI policy frameworks
- Strong familiarity with AI\-native engineering workflows and how coding assistants can change prototyping, product development, and team operating models
- Ability to shape role\-based AI literacy and training programs in partnership with HR or learning teams
- Bias for action, clarity, and lean execution
Suggested Backgrounds
Strong candidates may come from backgrounds such as:
- AI strategy and transformation leadership
- enterprise innovation and incubation leadership
- applied AI product or platform leadership
- technology program leadership with strong AI and governance depth
The minimum and maximum full\-time annual salary for this role is listed below, by location. Please note that this salary information is solely for candidates hired to perform work within this location. Experience and education refers to LanguageLine Solutions’ current salary range for this position. US CA Monterey Headquarters and US (Remote) pay range is $175,000\.00 \- $190,000\.00 USD Annually. This role is also eligible for bonus potential. In addition, we offer a comprehensive benefits package including medical, dental, vision, and a 401(k) plan.
Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate’s offer letter.
We expect to accept applications for 60 days from posting date.
Interested candidates are encouraged to apply by visiting our careers site, where you can review the full job description and submit your application online.
TP Core Skills Taxonomy
- AI Proficiency
- Collaboration
- Communication
- Critical Thinking
- Data Literacy
- Emotional Intelligence
- Entrepreneurship
- Open\-Mindedness
- Process Excellence
- Solution Orientation
Work Environment: Remote
Travel Requirements: 10%
Physical Requirements: Office Clerical Category
Compliance Requirements: Support LLS’ Quality Management System (QMS) to continually improve the Division’s processes, procedures, and services; and thereby increase efficiency, productivity, effectiveness, and customer satisfaction
For U.S. Positions: Candidates must be authorized to work in the US without the need for visa sponsorship. At this time,
Teleperformance Specialized Services Companies does not offer visa sponsorship for this position.
Equal Opportunity Employer. All qualified applicants will receive consideration for employment and will not be discriminated against based on race. color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, age, pregnancy, genetic information or any other consideration prohibited by law or contract.
Compliance with Disability Laws. It is the policy of LanguageLine that qualified individuals with disabilities not be discriminated against because of their disabilities in regard to job application procedures, hiring, and other terms and conditions of employment. It is also our policy to provide reasonable accommodations to qualified individuals with disabilities in all aspects of the employment process. We are prepared to modify or adjust the job application process or the job or work environment to make reasonable accommodations to the known physical or mental limitations of the applicant or employee to enable the applicant or employee to be considered for the position he or she desires, to perform the essential functions of the position in question, or to enjoy equal benefits and privileges of employment as are enjoyed by other similarly situated employees without disabilities, unless the accommodation will impose an undue hardship.
VEVRAA Federal Contractor requesting appropriate employment service delivery systems, such as state workforce agencies and local employment delivery systems, to provide priority referrals of protected veterans.
PAY TRANSPARENCY NONDISCRIMINATION PROVISION
The contractor will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the contractor’s legal duty to furnish information. 41 CFR 60\-I.35(c)
Salary Context
This $175K-$190K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At LanguageLine Solutions, 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 $181,170 based on 12,692 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800. Disclosed range: $175K to $190K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
LanguageLine Solutions AI Hiring
LanguageLine Solutions has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $190K - $190K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% of all AI roles offer remote work.
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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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