Remote AI CoE Lead: Spec-Driven Prototyping & Architecture

SRM Technologies; Polska; Hybrid; PLN 469,000 - 704,000; Full time

Benefits offered by this job

Executive compensation

Equity options

Job summary

Zohorecruit is seeking an AI CoE Lead to drive architecture, engineering, and deployment of AI-native solutions across Pharma, HiTech, and BFS. You will lead Forward Deployed Engineers embedded within client workflows to translate specs and prototypes into production-ready software.

Partner with the CTO and Head of Sales, champion a spec-driven development approach, and ensure models achieve sustained daily usage while scaling MLOps for rapid go-lives.

Qualifications

• Spec-driven development with AI tools and rapid prototyping.
• Enterprise AI/ML deployment across Pharma, HiTech, and BFS.
• 2+ years in AI/ML or enterprise software with client-facing roles.
• Deep knowledge of AI architecture, LLMs, RAG, NLP, and MLOps.
• Ability to translate complex specs for C-suite buyers.

Responsibilities

• Spec-Driven Development & AI Prototyping: build rapid, high-fidelity prototypes from specs.
• AI Architecture & Industry Solutions: drive tech vision across Pharma/HiTech/BFS.
• Lead Forward Deployed Engineers: hire, mentor, and scale client-facing FDEs.
• Sales & Demo Partnership: collaborate on enterprise pitches with technical demos.
• Drive Workflow Adoption: ensure daily model usage and translate feedback into specs.
• Standardize Infrastructure: establish MLOps practices to accelerate go-lives.

Skills

Spec-Driven Development

AI/ML Expertise

Executive Communication

Client-facing Leadership

Agile Delivery

Tools

MLOps

LLM Tools

RAG/NLP

Job description

Zohorecruit is seeking an AI CoE Lead to drive architecture, engineering, and deployment of AI-native solutions across Pharma, HiTech, and BFS. You will lead Forward Deployed Engineers embedded within client workflows to translate specs and prototypes into production-ready software.

Partner with the CTO and Head of Sales, champion a spec-driven development approach, and ensure models achieve sustained daily usage while scaling MLOps for rapid go-lives.