Senior Software Engineer - AI/ML Platform

Responsibilities

• Design and implement a scalable AI/ML platform that seamlessly integrates with existing CI/CD pipelines and data platforms, ensuring optimal performance and reliability
• Lead the design and development of a unified chatbot platform backed by multiple agents, implementing RBAC systems for secure agent/LLM access control
• Build a comprehensive AI agent ecosystem with scalable agentic operations, including frameworks for iterative agent improvement with measurable performance metrics for instructions, tools, and prompts etc.
• Develop and implement innovative LLM applications (RAG, Agentic workflows) to enhance productivity, system efficiency, and cost-effectiveness across Fanatics ecosystem
• Research, evaluate, and implement no-code agent orchestration tools to democratize AI workflow creation across the organization
• Implement end-to-end user interaction tracking, audit logging, and cost attribution systems with monthly reporting capabilities for team usage monitoring
• Create comprehensive technical documentation, architecture/design documents, developer guides, and operational procedures to ensure knowledge transfer and system maintainability
• Collaborate with data scientists, software engineers, and cross-functional teams while mentoring junior members and leading code reviews to maintain high quality standards
  

Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, or related field with 5+ years industry experience including 2+ years in AI/ML operations/platform development
• Proficiency in programming languages (Java/Python/GoLang/Scala) and ML libraries (TensorFlow, PyTorch, Scikit-learn) with hands-on Terraform infrastructure as code experience
• Experience with Data governance systems, like Datahub and MCP (Model Context Protocol) server integrations
• Experience working with large language models (LLMs), generative AI, and platforms like AWS Bedrock, Google Vertex AI, or similar cloud AI services
• Experience with Kubernetes container orchestration, Airflow DAGs for workflow management, and agentic AI systems or multi-agent orchestration
• Knowledge of role-based access control (RBAC) systems, security best practices for AI platforms, and cost optimization for cloud-based AI/ML services
• Advanced knowledge of toolkits for data scientists, including RStudio, Sagemaker, Lambda, S3, Glue, Redshift, EC2, IAM etc.
• Strong problem-solving, analytical, communication, and collaboration skills with ability to work effectively in cross-functional team environments
• Proven track record of technical leadership, mentoring capabilities, and driving innovation in AI/ML technologies and MLOps practices