Ai Architect

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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