AI/ML Tech Lead, Forge

• 6+ years in MLOps, ML engineering, MLOps, and data science with 2+ years leading and mentoring agile teams. Proven expertise in deploying essential MLOps components like CI/CD, model registry, and input loading
• Skilled in creating and launching diverse models (statistical, LLM, GenAI, GPT 3.5/4.0) from POC to production
• Proven ability to manage projects and programs, leading small teams (2-5 members) and directly handling data analysis, modeling, data pipeline setup, and MLOps
• Skilled in AIML, using platforms like AWS, Amazon Bedrock, and Snowflake, and proficient in Python, SQL, Tableau, and Alteryx
• 3+ years of experience working with Jupyter Notebooks, Git, GitHub, JIRA, and weights & biases, enhancing collaboration and version control in project development
• Significant collaboration with research teams, contributing hands-on research skills that advance project outcomes
• Previous experience working in Private Markets, Financial Services or FinTech space

Desirable

• As our Tech Lead in AI/ML, you will play a crucial role in the hands-on development of mentoring peers and delivery of AI products powered by our internal AI models and machine learning pipelines
• You'll identify new opportunities for AI solutions throughout the organization, and set the direction and roadmap required to implement MLOPS
• You will partner with the team through the creation of AI models, and develop, and deploy LLM-based applications in production, evaluating and improving their performance and accuracy over time
• Collaborating closely with Product, Research, and Engineering teams, you’ll ensure seamless integration of AI technologies into our products, setting new standards for data quality and model performance
• Design, develop, and maintain a AI/ML platform that is accurate, secure, and fast
• Lead workstreams within a lean and agile environment from requirements gathering to creating a plan of actionable tasks for the team
• Provide coaching, and mentorship to members of the data team on architecture, software development best practices, and new technologies
• Establish evaluation criteria for data quality tracking and production ML model tracking
• Partner with product, research, and engineering to ensure product changes integrate effectively