AI/ML Engineer, Software Engineer + GenAI

Role: AI Engineer Location: 100% Remote Work Duration: Contract Role Immediate Interview Availability: Open for interviews starting tomorrow afternoon, between 1:00 PM and 5:00 PM CST. AI Engineer (Software Engineer + GenAI experience, mid-level, 3-5 years of experience) Tech Skills: • Python & AWS: Essential backend and cloud infrastructure foundation. • UI/UX Skills: Required for front-end platform components (25% of the role). • Gen AI Platform: Building and enhancing core services for their proprietary GenAI platform. • Nice to have: Financial Services Background Job Description The Fitch Group Emerging technology AI Center of excellence (COE) group is seeking a senior machine learning engineer to be part of a team that will be dedicated to build and support Generative AI, Machine learning, Deep Learning and Data science solutions across the organization. Specific Emerging Technology team objectives are: • Gen AI/ML technology implementation with business and product owners • AI COE function to provide standards, prescriptions, reusable frameworks and platform for AI acceleration • Emerging Tech Governance function covering policies, guidelines and processes to govern AI/ML enabled components as well as third party AI governance • Lead and support other enterprise-level AI exploration tools and capabilities • Provide guidance and support for safe development and deployment of AI Core project specific Objectives: - Collect the core compliance and regulatory requirements from the Fitch compliance and risk team for building AI guardrails -Design the Gen UI/UX and backend for these "Guardrail" requirements -Implement PII/MNPI and Prompt injection handling capabilities in the "Guardrail" backend AI COE Specific objectives: • Designs and develops scalable solutions and GenAI and ML workflows that leverage machine learning, gen AI and deep learning models to meet enterprise requirements. • Actively participate in maintaining SLAs for Production applications and support. Create metrics to continuously evaluate the performance of machine learning solutions and improve the performance of existing machine learning solutions • Build with AWS and Azure cloud computing services providing the necessary infrastructure, resources, and interfaces to enable data loading and LLM workflows. • Use Python/Java and large-scale data workflow orchestration platforms e.g. Airflow to construct software artifacts for ETL, interfacing with diverse data formats and storage technologies, and incorporate them into robust data workflows and dynamic systems