Artificial Intelligence & Expert Systems

This is a hands-on individual contributor role focused on building AI/agentic systems for an Enterprise Data Platform, with emphasis on multi-agent orchestration on Google Cloud Platform (Google Cloud Platform)

Key expectations include:

• Architecture: Design multi-agent AI systems, evaluate trade-offs (single vs. multi-agent, RAG vs. fine-tuning), and contribute to Architecture Decision Records.
• Hands-on coding: Daily production-grade code across agent frameworks, backend/frontend, LLM-powered workflows (NL-to-SQL, semantic search, metadata enrichment), plus guardrails and observability for AI outputs. Familiarity with Agentic coding tools like opencode, Claude code etc is expected.
• Full-stack work: Backend (Python/FastAPI) and frontend (Angular/React) development, chat and API interfaces, and evaluation/benchmarking tools, with end-to-end feature ownership.
• Engineering excellence: Maintain high code quality, lead by example in reviews, conduct root-cause analysis on agent failures, and act as the team’’s technical anchor for tough problems.
• Collaboration: Work with Product, Data Engineering, and Platform teams; mentor others, support sprint planning, and help onboard new hires. In short, the role combines deep technical architecture responsibility with daily hands-on coding, centered on building robust, scalable multi-agent AI systems and serving as the team’’s go-to technical expert.

• Artificial Intelligence & Expert Systems

Skills Preferred:

• Google Cloud Platform

Experience Required:

• Bachelor’s degree in Computer Science, Engineering, Data Science, or a related technical field.
• 5+ years of professional software engineering experience with demonstrated hands-on coding proficiency.
• Demonstrable experience building AI-powered applications or operating LLM-based systems in production environments.
• Proven ability to interpret ambiguous requirements and independently deliver functional, well-tested software.

Experience Preferred:

• Experience with the Google Agent Development Kit (ADK) or comparable agent frameworks, such as CrewAI, or LangGraph.
• Familiarity with data engineering practices and data governance processes.
• Applied machine learning experience encompassing embeddings, classification, clustering, natural language processing, and evaluation metrics.