Ai Architect

Grid Dynamics; Aveiro; Presencial; EUR 120 000 - 180 000; Tempo integral

Resumo da oferta

Grid Dynamics is seeking a visionary AI Architect to design, build, and deploy enterprise-grade AI production systems, bridging advanced AI research with scalable software.

You will lead multi-cloud deployments on AWS and Azure, craft data architectures for LLM pipelines, and guide teams in AI engineering, MLOps, and production-ready practices.

Qualificações

• Hands-on Python expertise with AI/ML libraries.
• Experience deploying AI solutions on AWS and Azure.
• Proficient with multi-agent orchestrators and OpenAI Agents SDK.
• Deep experience with Databricks and Snowflake for AI pipelines.
• Designing and deploying Retrieval-Augmented Generation systems.

Responsabilidades

• Architect, implement, and scale enterprise AI production systems.
• Lead cloud infra and MLOps decisions for AI workloads.
• Develop data architectures feeding LLM pipelines.
• Write production Python code and contribute to core codebase.
• Guide teams on best practices for AI engineering and LLMOps.

Conhecimentos

Python

Multi-Cloud

LLM deployment

MLOps

Architectural design

OpenAI Agents SDK

Databricks

Snowflake

Ferramentas

Databricks

Snowflake

Azure OpenAI Service

SageMaker

Bedrock

Descrição da oferta de emprego

We are looking for a visionary and hands-on AI Architect to design, build, and deploy enterprise-grade AI production systems. In this role, you will bridge the gap between complex AI research and scalable, production-ready enterprise software.

Responsibilities

• Architect & Implement: Design and implement robust, scalable, and secure Generative AI solutions, including production-grade RAG architectures and multi-agent systems.
• Cloud Infrastructure Management: Architect AI workloads on cloud infrastructure, leveraging both AWS and Microsoft Azure services seamlessly.
• Data Strategy & Engineering: Design and optimize data architectures tailored for AI, integrating enterprise data platforms to feed LLM pipelines.
• Hands-on Development: Write production-quality Python code, build prototypes, and actively contribute to the core codebase alongside engineering teams.
• Model Deployment & Optimization: Select, fine-tune, optimize, and deploy open-source and proprietary Large Language Models (LLMs) effectively.
• Technical Leadership: Guide engineering teams on best practices for AI engineering, MLOps, LLMOps, and cloud infrastructure selection.

Qualifications

• Hands-on Python Expertise: Exceptional programming skills in Python, including deep familiarity with AI/ML libraries, asynchronous programming, and API development.
• Multi-Cloud Proficiency: Proven experience architecting and deploying AI solutions on both Microsoft Azure and AWS (e.g., AWS Bedrock, SageMaker, Azure OpenAI Service, Azure Machine Learning).
• Agentic Frameworks: Hands-on experience building autonomous workflows using advanced multi-agent orchestrators, specifically the OpenAI Agents SDK, Pydantic and similar.
• Enterprise Data Platforms: Deep technical expertise in integrating, querying, and managing data specifically within Databricks and Snowflake for AI training and inference context.
• Advanced RAG Architectures: Proven track record of designing and deploying sophisticated Retrieval-Augmented Generation systems, including experience with vector databases, advanced chunking strategies, hybrid search, and reranking mechanisms.

Nice-to-Have

• Conversational AI: Experience building, deploying, and optimizing enterprise chatbots and virtual assistants is a major plus.
• Open-Source LLM Deployment: Experience serving and optimizing open-source models (e.g., Llama, Mistral) using frameworks like vLLM, Hugging Face TGI, or Ollama.
• Experience with Temporal: Experience creating long running durable AI agents using Temporal will be a major plus

About Us

Grid Dynamics is a leading provider of technology consulting, agile co-creation, and scalable engineering services for Fortune 500 enterprises undergoing digital transformation. We combine business acumen with cutting-edge engineering to help enterprises deploy AI at scale, modernize core platforms, and implement next-generation cloud architectures.