# Applied AI Engineer – Agentic Workflows

**Company:** [Cohere](https://hotfix.jobs/companies/cohere)
**Location:** San Francisco, CA
**Role:** ML Engineering
**Skills:** Python, TypeScript, LLMs, React, Plan-And-Execute, RAG, Pinecone, Weaviate, LangGraph, Crewai
**Posted:** 2026-01-06

> Designs, builds, and deploys production-grade AI agents using LLMs for enterprise workflows. Collaborates with customers to solve business problems, ensures reliability, and mentors teams on agentic architectures.

## Job Description

## Why this role?

We’re a fast-growing startup building **production-grade AI agents for enterprise customers** at scale. We’re looking for Software Engineers with Applied AI experience who can own the design, build, and deployment of **agentic workflows powered by Large Language Models (LLMs)**—from early prototypes to production-grade AI agents, to deliver concrete business value in enterprise workflows.

In this role, you’ll work closely with customers on real-world business problems, often building first-of-their-kind agent workflows that integrate LLMs with tools, APIs, and data sources. While our pace is startup-fast, the bar is enterprise-high: agents must be **reliable, observable, safe, and auditable** from day one.

You’ll collaborate closely with customers, product, and platform teams, and help shape how agentic systems are built, evaluated, and deployed at scale.

## What You’ll Do

**Customer-Facing Technical Impact**
- Work closely with enterprise customers to translate high-value, ambiguous business problems into well-framed agentic problems with clear success criteria and evaluation methodologies.
- Provide technical leadership across the full development and evaluation lifecycle, including post-deployment iteration, for agentic workflows.
- Contribute to shared frameworks and patterns that enable consistent delivery across customers.

**Agent Design, Build and Production launches**
- Lead the design, build, and delivery of LLM-powered agents that reason, plan, and act across tools and data sources with enterprise-grade reliability and performance.
- Balance rapid iteration with enterprise requirements, evolving prototypes into stable, reusable solutions.
- Define and apply evaluation and quality standards to measure success, failures, and regressions.
- Debug real-world agent behavior and systematically improve prompts, workflows, tools, and guardrails.

**Team Mentorship & Organizational Impact**
- Mentor engineers across distributed teams.
- Drive clarity in ambiguous situations, build alignment, and raise engineering quality across the organization.

## Required Skills & Experience

**Technical Foundations & Applied AI**
- **Production Engineering:** Substantial experience building, shipping, and maintaining production-grade software (Python/TypeScript). You understand how to write clean, testable, observable and scalable code.
- **Agentic Architectures:** Hands-on experience building agents that plan and execute multi-step tasks (ReAct, Plan-and-Execute) and interact with external APIs/tools.
- **The LLM Stack:** Deep familiarity with Frontier Models (GPT, Claude, Gemini), RAG, vector databases (Pinecone, Weaviate, etc.), and orchestration frameworks (LangGraph, CrewAI, or custom state machines).
- **Rigorous Evaluation:** Proven ability to move beyond "trial and error" by building robust evaluation frameworks to measure agent accuracy, safety, and latency.

**Leadership & Impact**
- **Stakeholder Mastery:** Experience leading technical discussions with enterprise customers to translate ambiguous business needs into concrete technical specs.
- Experience mentoring distributed teams and setting the architectural standards for AI/Agentic systems.

## Additional Requirements
- Strong written and verbal communication skills.
- Ability and interest to travel up to 25%, flexible.

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