Ai Architect

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.

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