About PureFacts Financial Solutions

PureFacts is the leader in the Revenue Performance Management category for wealth and asset management firms. The PureRevenue™ Platform helps organizations maximize revenue potential by connecting pricing, billing, compensation, advisor behavior, and AI-powered intelligence within a single Revenue Book of Record. By transforming fragmented revenue processes into a coordinated growth system, firms gain greater visibility, stronger pricing discipline, improved revenue capture, and more effective advisor alignment. The result is faster organic growth, improved profitability, and increased enterprise value. For more than 25 years, PureFacts has helped leading financial institutions turn revenue from an operational process into a strategic advantage.

At PureFacts, we are building an AI-native platform and company. We embed AI, intelligent automation, and agentic workflows across our products and operations to detect anomalies, surface insights, streamline repetitive work, and support faster, better decision-making. In a highly regulated industry, we believe AI must be practical, governed, and auditable—amplifying human expertise while helping our teams and clients focus on higher-value, strategic work.

About the role

As our Machine Learning/AI Tech Lead, you will provide technical direction and oversight to a team of ML and AI engineers, and own overall ML/AI product vision, including platform and ops, for 3-5 concurrent client models. You will translate local models into robust enterprise grade products, set ML roadmap from Pricing Score through Revenue Assistant Agent. You will lead the design, development, and deployment of AI and machine learning solutions across the PureFacts platform. This role sits at the intersection of data science, engineering, and product, driving the integration of AI into core products and internal processes.

You will play a key role in advancing PureFacts’ AI-first strategy, building scalable solutions that automate workflows, reduce operational friction, and enhance client outcomes This includes identifying opportunities to replace manual processes with intelligent automation and delivering AI capabilities that create measurable business impact.

What you'll do

AI-First Strategy & Automation

• Champion PureFacts’ AI-first approach, embedding AI across products, workflows, and internal operations
• Identify and prioritize opportunities to eliminate manual, repetitive, and low-value work through automation
• Partner with Product and Leadership to define AI initiatives that improve efficiency, scalability, and decision-making
• Drive adoption of AI capabilities that enable teams and clients to focus on higher-value activities

Model Development & Intelligent Systems

• Design, build, and deploy machine learning and AI-driven solutions in production environments
• Develop capabilities such as:
• • Predictive analytics and forecasting
  • Intelligent automation of reporting and data workflows
  • NLP and generative AI for insights and client communication
  • Anomaly detection in financial and operational data
• Ensure solutions deliver tangible efficiency gains and measurable business outcomes

AI Engineering & MLOps

• Establish best practices for MLOps, model lifecycle management, and deployment pipelines
• Build scalable systems that support continuous learning, monitoring, and optimization
• Implement automation in model deployment, testing, and performance tracking
• Leverage cloud platforms (Azure-based) to scale AI capabilities efficiently

Data & Platform Integration

• Collaborate with data engineering teams to ensure high-quality, accessible data pipelines
• Integrate AI capabilities into PureFacts’ SaaS platform and client-facing products
• Enable seamless delivery of AI-powered features through APIs and microservices architecture

Leadership & Mentorship

• Lead and mentor a team of machine learning engineers and data scientists
• Foster a culture of innovation, experimentation, and continuous improvement
• Encourage the use of AI tools and automation to improve team productivity and output

Cross-Functional Collaboration

• Partner with Product, Engineering, and Client teams to translate AI capabilities into real-world value
• Help stakeholders identify opportunities to streamline processes and reduce manual effort
• Communicate AI strategy and outcomes clearly to both technical and non-technical audiences

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
  • Transparency and auditability

Qualifications

Experience

·8-10 yrs ML engineering experience, including MLOps (model registries, feature stores, retraining pipelines).

• Experience in SaaS, fintech, or data-driven environments
• Proven track record of delivering production AI solutions that drive efficiency or automation
• Experience leading or mentoring technical teams

Technical Skills

• Strong programming skills in Python
• Experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn)
• Experience with:
• • Data pipelines (SQL, Spark)
  • APIs and microservices
  • Cloud platforms (AWS, Azure, GCP)
• Familiarity with MLOps tools and automation frameworks

AI & Automation Mindset

• Strong focus on using AI to drive efficiency and eliminate manual work
• Experience implementing automation and intelligent workflows
• Familiarity with LLMs and generative AI tools is highly preferred

Leadership & Communication

• Ability to lead complex technical initiatives and influence stakeholders
• Strong communication skills, with the ability to translate AI into business value
• Experience working in agile, fast-paced environments

·People management experience a plus

Education

• Degree in Computer Science, Data Science, Engineering, or related field
• Advanced degree preferred but not required

Key Success Metrics

• Reduction in manual effort and operational inefficiencies through AI
• Successful deployment of AI-driven features into production
• Measurable improvements in client outcomes and internal productivity
• Adoption of AI capabilities across teams and workflows
• Scalable, reliable, and high-performing AI systems