AI Architect

IndiaRemote-friendlyFull timePosted 100 days ago

Own the technical vision for enterprise-scale AI — the architecture, the standards, and the case for why it is safe to build this way.

What you’d do

Design end-to-end AI/ML architectures for enterprise clients across various industries and use cases
Lead technical strategy for AI implementations including model selection, infrastructure design, and deployment approaches
Architect scalable machine learning pipelines, MLOps frameworks, and model governance systems
Evaluate and recommend AI platforms, frameworks, and tools (OpenAI, Anthropic Claude, Azure AI, AWS SageMaker, Google Vertex AI)
Design data architectures that support AI workloads including data lakes, feature stores, and vector databases
Develop AI governance frameworks ensuring ethical AI practices, bias mitigation, and responsible deployment
Create technical specifications, architecture diagrams, and implementation roadmaps for AI projects
Lead proof of concepts and pilot implementations to validate architectural decisions
Collaborate with data scientists, ML engineers, and stakeholders to translate business requirements into technical solutions
Design integration patterns for embedding AI capabilities into existing enterprise systems
Establish best practices for model monitoring, versioning, and continuous improvement
Mentor technical teams on AI/ML architecture principles and emerging technologies

Deep expertise in machine learning frameworks (TensorFlow, PyTorch, scikit-learn, Hugging Face)
Extensive experience with Large Language Models (LLMs) and generative AI technologies
Strong knowledge of AI platforms (OpenAI API, Anthropic Claude, Azure OpenAI, AWS Bedrock, Google Vertex AI)
Proven track record of architecting and deploying production-grade AI systems at scale
Experience with MLOps tools and practices (MLflow, Kubeflow, Weights & Biases, Neptune.ai)
Deep understanding of vector databases and retrieval systems (Pinecone, Weaviate, Chroma, FAISS)
Expertise in cloud platforms (AWS, Azure, GCP) and their AI/ML services
Knowledge of prompt engineering, RAG (Retrieval Augmented Generation), and fine-tuning techniques
Strong understanding of data engineering and big data technologies (Spark, Airflow, Kafka)
Experience with model deployment and serving (TensorFlow Serving, TorchServe, Triton, FastAPI)
Familiarity with responsible AI principles, fairness, explainability, and bias mitigation
Understanding of security and privacy considerations in AI systems (data privacy, model security)

Five questions, and no résumé. You’ll start with the manifesto and the handbook, then come straight back to this one — no need to find it again.