Director of AI

Job summary

You will lead the design, development, and deployment of AI and machine learning solutions while providing technical direction to a team of engineers. The role involves driving an AI-first strategy to automate workflows and integrate intelligent systems into the company's core SaaS platform.

Job details

ABOUT THE ROLE We are seeking a Director of ML & AI to provide senior-level leadership in executing the vision, strategy, and governance of user-facing artificial intelligence products across PureFacts — and to personally build and ship the AI capabilities that deliver it. You will lead the design, development, and deployment of AI and machine learning solutions across the PureFacts platform, owning the ML/AI roadmap for AI-powered intelligence across the PureRevenue platform. You’ll build a scalable, enterprise-grade, multi-tenant ML/AI platform supporting training, inference, and evaluation pipelines at scale. You will lead and manage a team of 3-4 ML and ML & AI engineers who report directly to you — while building and shipping yourself. This role sits at the intersection of data science, engineering, and product, and plays a key part in advancing PureFacts' AI-first strategy: automating workflows, reducing operational friction, and delivering AI capabilities that create measurable business impact for our clients. WHAT YOU'LL DO AI Strategy & Direction * Define, execute, and own PureFacts' ML & AI vision and strategy, ensuring alignment with business objectives and measurable outcomes * Act as the AI advisor to senior leadership, informing investment decisions, client commitments, and external partnerships * Champion our AI-first approach, identifying and prioritizing opportunities to eliminate manual, repetitive, and low-value work through intelligent automation Build & Ship * Personally design, build, and deploy machine learning and AI-driven solutions in production — from prototype through deployment * Develop capabilities including generative AI and agentic systems, RAG pipelines, predictive analytics and forecasting, anomaly detection in financial and operational data, and intelligent automation of reporting and data workflows * Lead LLM model selection and ancillary build-vs-buy decisions based on systematic evaluation pipelines and frameworks * Translate proof-of-concept models into scalable, reliable, enterprise-grade product capabilities AI Engineering & MLOps * Establish best practices for MLOps, model lifecycle management, and deployment pipelines, including model registries, feature stores, and retraining pipelines * Build scalable agent architectures that support continuous learning, monitoring, cost optimization, and performance tracking * Leverage our Azure-based cloud platform (Azure ML, Azure AI Foundry) and Snowflake to scale AI capabilities efficiently * Establish success metrics, evaluation frameworks, and experimentation processes so quality claims can be defended to clients and regulators Data & Platform Integration * Collaborate with data engineering teams to ensure high-quality, accessible data pipelines * Integrate AI capabilities into the PureRevenue platform and client-facing products through APIs and microservices architecture Team & Cross-Functional Leadership * Hire, manage, and develop a team of 3-4 ML/AI engineers, owning performance, growth, and technical direction while fostering a culture of innovation, experimentation, and continuous improvement * Partner with Product, Engineering, and Client teams to translate AI capabilities into real-world value * Communicate AI strategy, capabilities, and limitations clearly to technical, executive, and client audiences, setting realistic expectations in a regulated industry Governance, Risk & Responsible AI * Ensure AI solutions adhere to data privacy, security, and regulatory requirements * Implement responsible AI practices including bias detection and mitigation, model explainability, and transparency and auditability WHAT YOU'LL NEED TO BE SUCCESSFUL Experience * 10+ years of ML/AI engineering experience, including MLOps (model registries, feature stores, retraining pipelines) * Advanced degree in Computer Science, Data Science, Engineering, or a related field * A track record of personally shipping production ML products that drove measurable business outcomes — shipped product is the proof of hands-on ability we're looking for * Direct experience building with LLMs, RAG, and agentic AI systems, including orchestration frameworks and model evaluation * Experience in SaaS, fintech, or data-driven enterprise environments * Experience leading engineering teams — as a people manager or hands-on tech lead — while continuing to build along side the team. Technical Skills * Strong programming skills in Python, used currently and regularly * Experience with AI (ADKs, LangSmith, etc.) and ML frameworks (PyTorch, TensorFlow, Scikit-learn, MLflow, SageMaker, Azure ML, or equivalent). * Experience with data pipelines (SQL, Spark), APIs, and microservices * Experience with cloud platforms (Azure preferred; AWS or GCP acceptable) * Experience with AIOps and MLOps tools and automation frameworks Mindset & Communication * Strong focus on using AI to drive efficiency and eliminate manual work * Ability to lead complex technical initiatives and influence stakeholders across functions * Strong communication skills, with the ability to frame AI investments in term of business value for executive and non-technical audiences * Comfort operating in an agile, fast-paced, scaling environment: strategic one hour, shipping code the next A Plus if you have * Formal people management experience (performance reviews, hiring, career development) * Experience in wealth management, asset management, or adjacent financial services technologyFamiliarity with AI governance frameworks and the evolving regulatory landscape for AI in financial services Key Success Metrics * Successful deployment of ML& AI-driven features into production, on a scalable and reliable platform * Reduction in manual effort and operational inefficiencies through AI, internally and for client * Measurable improvements in client AI product adoption and business outcomes * Evaluation and governance standards that stand up to client and regulatory scrutiny

What you’ll do

You will lead the design, development, and deployment of AI and machine learning solutions while providing technical direction to a team of engineers. The role involves driving an AI-first strategy to automate workflows and integrate intelligent systems into the company's core SaaS platform.

Requirements

Candidates must have 8-10 years of machine learning engineering experience, including strong proficiency in MLOps and cloud platforms like Azure. A degree in Computer Science, Data Science, or a related field is required, with a preference for advanced degrees.

Listed skills

• Machine learning · Preferred
• Python · Preferred
• SQL · Preferred
• Microsoft Azure · Preferred
• Leadership · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Machine Learning
• Artificial Intelligence
• MLOps
• Python
• TensorFlow
• PyTorch
• Scikit-learn
• SQL
• Spark
• Azure
• Cloud Platforms
• Microservices
• Predictive Analytics
• NLP
• Generative AI
• Leadership
• Influencing Skills
• Cross-Functional Collaboration
• Continuous Development
• Responsible AI
• Pipelines
• Financial Technology (FinTech)
• MLOps (Machine Learning Operations)
• Generative Artificial Intelligence
• Influencing Without Authority
• Intelligent Automation
• People Management
• Workflow Management
• Architectural Leadership
• AI Adoption
• Data Privacy
• Workflow Automation
• Application Programming Interface (API)
• Agile Methodology
• Amazon Web Services
• Anomaly Detection
• Automation
• Microsoft Azure
• Business Continuity Planning
• Business Valuation
• Decision Making
• Software As A Service (SaaS)
• Communication
• Computer Science
• Continuous Improvement Process
• Data Engineering
• Forecasting
• Governance
• Scalability
• Innovation

Job areas

• Technology
• Software
• Data & Analytics
• Engineering
• Management & Leadership
• Director of Machine Learning
• Data Scientist
• Systems Analysts
• Data Scientists

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