Lead ML/GenAI Architect for Data & AI Platforms

Grid Dynamics

Málaga

On-site

EUR 120,000 - 180,000

Full time

23 hours ago
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Benefits offered by this job

Competitive salary
Flexible schedule
Medical insurance
Well-equipped office
Professional development opportunities

Job summary

Grid Dynamics is seeking a seasoned Data & AI leader to shape target-state architecture for ML, GenAI and data platforms across European accounts, guiding discovery, pre-sales and delivery while building reusable assets.

The role mentors senior technologists, develops reference architectures and accelerators, and collaborates with sales to bring innovative data and AI solutions to market, ensuring scalable, cost-effective outcomes.

Qualifications

  • Proven experience designing production ML & GenAI systems end-to-end.
  • Deep expertise in ML platforms or GenAI/LLMOps with command of the other.
  • Strong hands-on experience with Azure and/or AWS ML services and data services.
  • Project experience with two of Databricks, Snowflake and Microsoft Fabric as data foundations.

Responsibilities

  • Own target-state architecture and technical oversight for key European data & AI accounts; set direction and review designs and code.
  • Help delivery teams resolve complex SQL and Spark problems hands-on.
  • Lead consulting engagements, architecture assessments and discovery workshops.
  • Lead technical pre-sales through RFP/RFI responses, solution proposals and delivery plans.

Skills

ML platforms & MLOps
GenAI & LLMOps
Azure & AWS ML services
Databricks & Snowflake

Tools

Databricks
Snowflake
Microsoft Fabric

Job description

Grid Dynamics is seeking a seasoned Data & AI leader to shape target-state architecture for ML, GenAI and data platforms across European accounts, guiding discovery, pre-sales and delivery while building reusable assets.

The role mentors senior technologists, develops reference architectures and accelerators, and collaborates with sales to bring innovative data and AI solutions to market, ensuring scalable, cost-effective outcomes.

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