Job Description

Role Summary

Work in a scaled Agile working environment
Be part of a global and diverse team
Contribute to all stages of software development lifecycle
Participate in peer reviews of solution designs and related code
Maintain high standards of software quality within the team by following good practices and habits
Use frameworks like Google Agent Development Kit Google ADK and LangGraph to build robust controllable and observable agentic architectures
Assist in the design of LLMpowered agents and multiagent workflows planning tool use orchestration memory and human in the loop
Lead the implementation deployment and test of multiagent systems
Mentor junior engineers on best practices for LLM engineering and agentic system development
Drive technical discussions and decisions related to AI architecture and framework adoption
Proactively identify and address technical debt and areas for improvement in AI systems
Represent the team in cross functional technical discussions and stakeholder meetings

Key Responsibilities

Design and build complex agentic systems with multiple interacting agents
Implement robust orchestration logic state machines  graphs retries fallbacks escalation to humans
Implement RAG pipelines tool calling and sophisticated system prompts for optimal reliability latency and cost control
Apply core ML concepts to evaluate and improve agent performance including dataset curation and biassafety checks
Lead the development of agents using Google ADK andor LangGraph leveraging advanced features for orchestration memory evaluation and observability
Integrate with supporting libraries and infrastructure eg LangChainLlamaIndex vector databases message queues monitoring tools with minimal supervision
Define success metrics build evaluation suites for agents automatic  human evaluation and drive continuous improvement
Curate and maintain comprehensive prompttest datasets run regression tests for new model versions and prompt changes
Deploy and operate AI services in production establishing CICD pipelines observability logging and tracing
Debug complex failures endtoend identifying and document root causes across models prompts APIs tools and data
Work closely with product managers and stakeholders to shape requirements translate them into agent capabilities and manage expectations
Document comprehensive designs decisions and runbooks for complex systems

Must Have Qualifications
Education  experience

3 years of experience as Software Engineer  ML Engineer  AI Engineer with at least 1 or 2 years working directly with LLMs in real applications not just experiments or coursework
Bachelor s or Masters degree in Computer Science Engineering Mathematics or a related field or equivalent practical experience

Core technical skillsProgramming  software engineering

Strong proficiency in Python core language features packaging testing async type hints
Very strong software engineering practices version control Git unitintegration testing code reviews CICD
Experience building and consuming RESTgRPC APIs and integrating external toolsservices

Machine Learning good understanding

Data infra

Experience working with vector databases 
Experience deploying on at least one major cloud platform GCP AWS Azure and using managed services eg serverless runtimes container orchestration secrets management

Soft skills

Ability to translate ambiguous business requirements into concrete technical designs
Strong communication skills able to explain tradeoffs to both technical and nontechnical stakeholders
Comfort working in an experimental environment with rapid iteration but with a strong bias towards production quality and maintainability