AI Architect

Role overview Shape the technical direction for enterprise-grade artificial intelligence initiatives, defining architecture standards, governance practices, and the rationale for safe, responsible build decisions. The position blends hands-on architectural design with strategic leadership across diverse industries and use cases.

Responsibilities - Design end-to-end AI and machine learning architectures spanning model selection, infrastructure, deployment patterns, and integration into existing enterprise systems. - Lead technical strategy for implementations, including proofs of concept and pilot programs that validate architectural choices before scale-out. - Architect scalable ML pipelines, MLOps frameworks, and model governance systems that cover versioning, monitoring, and continuous improvement. - Evaluate and recommend AI platforms, frameworks, and tools across major providers, including Azure AI, AWS SageMaker, and Google Vertex AI. - Design supporting data architectures such as data lakes and feature stores, plus vector database structures for retrieval workloads. - Develop AI governance frameworks covering ethical practices, bias mitigation, explainability, and responsible deployment. - Mentor engineering teams on AI/ML architecture principles and emerging technologies.

Requirements - Deep expertise with machine learning frameworks such as TensorFlow, PyTorch, scikit-learn, and Hugging Face. - Extensive hands-on experience with large language models and generative AI technologies. - Strong knowledge of leading AI platforms, including OpenAI, Anthropic Claude, Azure OpenAI, AWS Bedrock, and Google Vertex AI. - Proven track record architecting and operating production-grade AI systems at scale. - Experience with MLOps tooling such as MLflow, Kubeflow, Weights & Biases, or Neptune.ai. - Deep familiarity with vector databases and retrieval systems, including Pinecone, Weaviate, Chroma, or FAISS. - Strong grasp of cloud platforms and their AI/ML services, plus prompt engineering, retrieval-augmented generation, and fine-tuning techniques. - Understanding of responsible AI principles, data privacy, and model security considerations.