What are the responsibilities and job description for the Senior AI Agentic Lead position at Vidorra Consulting Group?
Role: Senior AI Agentic Lead
Location: Mountain View, CA (Full-Time)
Serve as the primary technical point of contact for the client, embedded on-site
Design, build, and deploy AI agent workflows across engineering and other use cases e.g. PR review and acceleration, test generation, incident correlation & RCA, knowledge graphs, support triage, documentation refresh, etc.
Get trained on client-specific tools, platforms, and engineering processes, then cascade that knowledge to the Global team
Lead sprint-based delivery across a distributed team setting technical standards, reviewing outputs, and unblocking issues
Collaborate with the client's stakeholders to define scope, validate agent outputs, and iterate quickly
Work closely with other members of the team to incorporate platform knowledge into agent design
Identify automation opportunities across the developer journey and translate them into well-scoped, executable agent implementations
Operate the AI Agentic CoE governance cadence daily team sync, weekly KPI review, monthly steering with client sponsors
What We're Looking For
10 years software engineering, 2 3 years focused on AI / LLM-based systems
Must be hands-on actively building, coding, deploying, not just directing
Proven experience designing and building agentic systems multi-step reasoning, tool use, memory, orchestration
Strong proficiency with Claude (Anthropic API / Claude Agent SDK) and familiarity with other LLM providers (OpenAI, open-weight Llama / Mistral, cloud-native model platforms)
Agentic frameworks LangChain, LangGraph, AutoGen, CrewAI, OpenAI Agents SDK, or similar
Python proficiency; APIs, data pipelines, workflow automation
Production LLM patterns RAG, evaluations, guardrails, structured outputs, function calling, prompt engineering
Enterprise client work translating ambiguous requirements into working systems
Distributed team leadership (US offshore)
Strong communication skills
Nice to Have
Developer-platform / DevEx / SRE / observability engineering background
Identity / IAM / fraud / trust-and-safety domain exposure
Familiarity with enterprise tooling ecosystems (ticketing, dashboards, service graphs, deployment tools, observability stacks)
Prior consulting or embedded-engagement experience
Fintech, SaaS, or large-scale consumer-product engineering exposure
Location: Mountain View, CA (Full-Time)
Serve as the primary technical point of contact for the client, embedded on-site
Design, build, and deploy AI agent workflows across engineering and other use cases e.g. PR review and acceleration, test generation, incident correlation & RCA, knowledge graphs, support triage, documentation refresh, etc.
Get trained on client-specific tools, platforms, and engineering processes, then cascade that knowledge to the Global team
Lead sprint-based delivery across a distributed team setting technical standards, reviewing outputs, and unblocking issues
Collaborate with the client's stakeholders to define scope, validate agent outputs, and iterate quickly
Work closely with other members of the team to incorporate platform knowledge into agent design
Identify automation opportunities across the developer journey and translate them into well-scoped, executable agent implementations
Operate the AI Agentic CoE governance cadence daily team sync, weekly KPI review, monthly steering with client sponsors
What We're Looking For
10 years software engineering, 2 3 years focused on AI / LLM-based systems
Must be hands-on actively building, coding, deploying, not just directing
Proven experience designing and building agentic systems multi-step reasoning, tool use, memory, orchestration
Strong proficiency with Claude (Anthropic API / Claude Agent SDK) and familiarity with other LLM providers (OpenAI, open-weight Llama / Mistral, cloud-native model platforms)
Agentic frameworks LangChain, LangGraph, AutoGen, CrewAI, OpenAI Agents SDK, or similar
Python proficiency; APIs, data pipelines, workflow automation
Production LLM patterns RAG, evaluations, guardrails, structured outputs, function calling, prompt engineering
Enterprise client work translating ambiguous requirements into working systems
Distributed team leadership (US offshore)
Strong communication skills
Nice to Have
Developer-platform / DevEx / SRE / observability engineering background
Identity / IAM / fraud / trust-and-safety domain exposure
Familiarity with enterprise tooling ecosystems (ticketing, dashboards, service graphs, deployment tools, observability stacks)
Prior consulting or embedded-engagement experience
Fintech, SaaS, or large-scale consumer-product engineering exposure