Marathon TS · 1 month ago
AI Innovation Engineer
Marathon TS is seeking an AI Innovation Engineer to join a newly formed innovation-focused team responsible for exploring and validating cutting-edge AI solutions. The role involves developing proof-of-concepts and AI-powered applications to solve high-impact business problems, requiring a builder mentality and a passion for innovation.
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Responsibilities
Design, develop, and deploy proof-of-concept AI applications and intelligent agents
Build Agentic AI solutions using modern LLM frameworks and Google's AI ecosystem
Develop and test AI agents utilizing Agent Development Kit (ADK) and related technologies
Create prototypes that demonstrate business value and accelerate innovation initiatives
Work with structured and unstructured data sources, including BigQuery datasets
Experiment with emerging Generative AI technologies and evaluate new tools and frameworks
Collaborate with business and technical stakeholders to identify opportunities for AI-driven solutions
Quickly learn new technologies and independently drive projects from concept to prototype
Document findings, recommendations, and technical approaches for future production development
Qualification
Required
3+ years of software engineering, AI engineering, or machine learning experience
Hands-on experience developing Generative AI applications
Strong understanding of Large Language Models (LLMs), prompt engineering, and agent-based architectures
Experience developing software applications using Python
Experience with Google Cloud Platform (GCP)
Experience working with BigQuery and cloud-based data environments
Ability to rapidly prototype and iterate on new ideas
Strong problem-solving skills and ability to work independently
Preferred
Experience with Google's Agent Development Kit (ADK)
Experience with Vertex AI and Gemini models
Experience building AI agents and multi-agent workflows
Experience with Retrieval-Augmented Generation (RAG)
Familiarity with LangChain, LlamaIndex, or similar frameworks
Experience evaluating and optimizing LLM performance
Exposure to MLOps and cloud deployment best practices