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.
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