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Join a Team That’s Passionate About Making Lives Better!
At Bill Gosling Outsourcing, we believe that success starts with an amazing team. We are a global leader in outsourcing solutions, we focus on making lives better, one connection at a time. We provide tailored solutions to businesses around the globe, specializing in customer care, sales, and financial services. We’re looking for enthusiastic, driven individuals to join our dynamic work environment where fun meets results!
The Director, AI Data Operations is responsible for building, launching, and scaling our business' AI Data Operations practice, establishing the solutions, capabilities, partnerships, delivery models, and go-to-market strategy required to support organizations developing and deploying artificial intelligence at scale.
This role combines AI domain expertise, commercial leadership, solution design, practice development, and global operations experience. The Director will identify emerging opportunities across the AI development and deployment lifecycle and translate complex or evolving client requirements into scalable managed-service solutions.
The role will establish the company's capabilities across AI/ML data operations, training and evaluation datasets, data labeling and multimodal annotation, model evaluation, human-in-the-loop systems, reinforcement learning and human preference data, agentic AI operations, AI quality and safety, computer vision, autonomous systems, data governance, and production AI monitoring.
The Director will also serve as a senior external representative of the company within the AI ecosystem, developing relationships with AI-native companies, technology platforms, hyperscalers, autonomous-systems organizations, specialist AI-data providers, and enterprise AI teams.
Ultimately, the role is accountable for transforming AI Data Operations from an emerging market opportunity into a scalable and commercially viable company practice and revenue stream.What You'll Do:
Key Details:
Location: Remote role within the US
Salary: base salary of up to $200k + annual bonus
Working Hours: within EST
Ideally 5+ years of consulting and sales experience
Key Responsibilities:
1. Build the AI Data Operations Practice
- Define BGO’s AI Data Operations strategy, service portfolio, positioning, and go-to-market proposition.
- Identify high-value opportunities across the AI development, training, evaluation, deployment, and production lifecycle.
- Develop repeatable solutions, operating models, delivery methodologies, commercial constructs, governance frameworks, and implementation playbooks.
- Establish scalable capabilities across emerging AI operations categories.
- Evaluate capability gaps and recommend whether BGO should build, partner, or acquire capabilities required to support the practice.
- Develop the organizational, technology, talent, quality, governance, and delivery requirements necessary to scale AI Data Operations globally.
- Establish appropriate performance indicators and operational standards for AI Data Operations engagements.
2. Create and Win Client Opportunities
- Develop relationships with AI-native companies, technology platforms, hyperscalers, autonomous-systems companies, and enterprises deploying AI at scale.
- Engage senior stakeholders across AI, Data, Product, Technology, Operations, Procurement, and related functions.
- Identify and diagnose operational bottlenecks across AI development and production environments.
- Translate ambiguous or emerging client challenges into clearly defined and commercially viable operational solutions.
- Lead client discovery sessions, solution workshops, presentations, proposals, and commercial negotiations.
- Partner with Sales, Solutions, Operations, Technology, and other BGO functions to develop differentiated client propositions.
- Personally originate and support the closure of BGO’s initial generation of AI Data Operations engagements.
- Develop sustainable opportunity pipelines and new revenue streams for the practice.
3. Design AI-Native Operations Solutions
- Data collection, preparation, enrichment, curation, and validation.
- Training and evaluation dataset operations.
- Data labeling and annotation.
- Multimodal annotation across text, image, audio, and video.
- Model evaluation, benchmarking, and validation.
- Human preference data and reinforcement-learning workflows.
- Human-in-the-loop operations.
- AI exception management and escalation.
- AI quality assurance and output validation.
- Agent evaluation, monitoring, and escalation.
- Trust & Safety and policy-enforcement operations.
- Mapping, geospatial, and location-data operations.
- Computer vision data operations.
- Autonomous systems and robotics data operations.
- Red teaming and adversarial-testing operations.
- AI workflow monitoring.
- Continuous model-improvement operations.
4. Establish AI Quality, Governance and Operational Controls
- Establish quality frameworks appropriate for AI training, evaluation, and production operations.
- Incorporate data governance, privacy, security, and responsible-data-handling requirements into solution design.
- Develop quality-control and quality-assurance mechanisms for human-generated and human-validated AI data.
- Establish appropriate human-in-the-loop escalation and exception-management frameworks.
- Partner with BGO Technology, Information Security, Privacy, Legal, Risk, and Operations teams to ensure solutions meet applicable internal and client requirements.
- Establish production-monitoring frameworks that support ongoing quality measurement and continuous improvement.
5. Build Market Presence and Thought Leadership
- Represent BGO in strategic client and industry conversations regarding AI Data Operations.
- Develop strong market perspectives on how human operations will evolve as AI systems become increasingly autonomous.
- Develop client-facing thought leadership, solution narratives, use cases, presentations, and market perspectives.
- Monitor developments across AI models, agentic AI, autonomous systems, AI safety, data services, and emerging AI operations categories.
- Translate emerging technology trends into practical service opportunities for BGO.
- Position BGO as a credible operating partner within the AI and data ecosystem.
6. Build Strategic Partnerships and Ecosystem Relationships
- Develop partnerships across the AI technology and data ecosystem.
- Build relationships with AI-native companies, hyperscalers, AI platforms, data technology providers, autonomous-systems organizations, and specialist service providers.
- Evaluate partnership opportunities that accelerate BGO’s market entry or extend its capabilities.
- Develop partner-enabled solutions where appropriate.
- Maintain an external network that provides insight into emerging AI operating models, technologies, and client requirements.
7. Enable Global Delivery and Scale
- Work with global delivery teams to translate solution concepts into scalable operating models.
- Define workforce, workflow, quality, governance, technology, and performance requirements for new AI services.
- Support transition from pilot or proof-of-concept engagements into sustainable managed-service operations.
- Establish repeatable delivery standards and playbooks across locations.
- Ensure solutions appropriately balance automation, human judgment, quality, speed, scalability, risk, and commercial viability.
Why Join Us?
- Growth Opportunities: We believe in promoting from within and providing opportunities for career advancement.
- Comprehensive Training: We offer extensive paid training to ensure you’re equipped for success.
- Team-Oriented Culture: Work in a collaborative, supportive environment with peers who are passionate about what they do.
- Diversity & Inclusion: We celebrate the unique perspectives and contributions of all our employees.
- Fun Workplace: Join a vibrant team that knows how to have fun! From team engagement activities to social events, we foster a lively and inclusive work environment where you’ll build strong connections.
- State-of-the-Art Offices: Work in our modern, well-equipped offices designed to enhance collaboration and productivity.
- Rewarding Work: Help businesses grow while making a real difference in people’s lives!
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At Bill Gosling Outsourcing, we believe that diversity makes us stronger. We welcome applicants from all backgrounds and are committed to creating an inclusive and supportive workplace where everyone can thrive. Regardless of your race, gender, age, ability status, or any other characteristic, you are valued here. If you require accommodations at any stage of the hiring process, we are happy to work with you to ensure you have the support you need – just let us know.
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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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Bill Gosling Outsourcing, 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 in Demand for This Role
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 $212,000 based on 11,866 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,250.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Bill Gosling Outsourcing AI Hiring
Bill Gosling Outsourcing has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Hampton, VA, US.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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