Job Summary

Main skills to be assessed:

• Software engineering: Python, OOP, ability to write code at a Mid/Senior level, not having worked exclusively in notebooks.
• Deep Learning in NLP: at least some contextual understanding, knowing where their work fits in the AI/ML landscape, and being able to use and process the necessary data in NLP.
• Generative AI: the core of the role—what an LLM is, how it works, what can be built around an LLM (RAG, MCP), multi-agent systems (LangChain / LangGraph / ADK in our case)

• 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 bias/safety checks.
• Lead the development of agents using Google ADK and/or LangGraph, leveraging advanced features for orchestration, memory, evaluation, and observability.
• Integrate with supporting libraries and infrastructure (e.g., LangChain/LlamaIndex, 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 prompt/test datasets; run regression tests for new model versions and prompt changes.
• Deploy and operate AI services in production, establishing CI/CD pipelines, observability, logging, and tracing.
• Debug complex failures end-to-end, 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.

Key Responsibilities

Main skills to be assessed:

• Software engineering: Python, OOP, ability to write code at a Mid/Senior level, not having worked exclusively in notebooks.
• Deep Learning in NLP: at least some contextual understanding, knowing where their work fits in the AI/ML landscape, and being able to use and process the necessary data in NLP.
• Generative AI: the core of the role—what an LLM is, how it works, what can be built around an LLM (RAG, MCP), multi-agent systems (LangChain / LangGraph / ADK in our case)

• 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 bias/safety checks.
• Lead the development of agents using Google ADK and/or LangGraph, leveraging advanced features for orchestration, memory, evaluation, and observability.
• Integrate with supporting libraries and infrastructure (e.g., LangChain/LlamaIndex, 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 prompt/test datasets; run regression tests for new model versions and prompt changes.
• Deploy and operate AI services in production, establishing CI/CD pipelines, observability, logging, and tracing.
• Debug complex failures end-to-end, 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.

Skill Requirements

Main skills to be assessed:

• Software engineering: Python, OOP, ability to write code at a Mid/Senior level, not having worked exclusively in notebooks.
• Deep Learning in NLP: at least some contextual understanding, knowing where their work fits in the AI/ML landscape, and being able to use and process the necessary data in NLP.
• Generative AI: the core of the role—what an LLM is, how it works, what can be built around an LLM (RAG, MCP), multi-agent systems (LangChain / LangGraph / ADK in our case)

• 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 bias/safety checks.
• Lead the development of agents using Google ADK and/or LangGraph, leveraging advanced features for orchestration, memory, evaluation, and observability.
• Integrate with supporting libraries and infrastructure (e.g., LangChain/LlamaIndex, 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 prompt/test datasets; run regression tests for new model versions and prompt changes.
• Deploy and operate AI services in production, establishing CI/CD pipelines, observability, logging, and tracing.
• Debug complex failures end-to-end, 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.

Other Requirements