Director, AI Data Operations

billgosling; United States; Remote; USD 120,000 - 200,000; Full time

Job summary

Bill Gosling Outsourcing in the United States is seeking a Director, AI Data Operations to build, launch, and scale our AI data operations practice, delivering scalable managed services across the AI development and production lifecycle.

This remote role requires 5+ years in consulting and sales, interacts with AI-native firms and hyperscalers, and aligns with EST hours; base salary can reach up to $200k with an annual bonus.

Qualifications

• 5+ years of consulting and sales experience.
• Experience selling AI/data operations solutions.
• Strong client-facing and stakeholder management.
• Ability to translate client needs into scalable services.

Responsibilities

• Define AI Data Operations strategy and service portfolio.
• Identify high-value opportunities across AI lifecycle.
• Develop repeatable solutions, operating models, governance frameworks, and playbooks.
• Establish scalable AI data operations capabilities globally.
• Lead client discovery sessions, workshops, proposals, and negotiations.
• Build relationships with AI-native companies, hyperscalers, and enterprises.
• Translate client challenges into actionable operational solutions.

Skills

Consulting experience

Sales experience

AI/ML domain knowledge

Business development

Job description

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