Principal AI Architect

Job Summary

We are seeking an experienced AI Architect to lead the design, implementation, and governance of end-to-end AI/ML solutions across our GenAI powered low-code/AI automation platform. The ideal candidate has a proven track record of building, deploying, and managing scalable AI/ML systems - from data ingestion and feature engineering to model deployment, monitoring, and optimization in production environments.

Responsibilities

• Architect and oversee AI/ML pipelines covering data collection, preparation, training, validation, and inference.
• Define and implement scalable AI infrastructure for training, deployment, and continuous integration (MLOps).
• Collaborate with data scientists, ML engineers, product manager, and product teams to translate business problems into AI-driven solutions.
• Establish frameworks for model governance, versioning, reproducibility, and explainability.
• Integrate models into production systems ensuring low latency, scalability, and reliability.
• Define data strategy, storage, and access patterns to support AI workloads.
• Build solutions to monitor model performance, drift, and data quality, implementing continuous retraining strategies.
• Ensure compliance with ethical AI, data privacy, and security best practices.
• Mentor AI/ML engineers and contribute to architectural decisions across the AI platform stack.

Requirements

• 12+ years of experience in data science, ML engineering and AI system architecture.
• Hands-on experience with Python, TensorFlow, PyTorch, Scikit-learn, spaCy and related AI/ML frameworks.
• Expertise in MLOps tools such as MLflow, Kubeflow, Vertex AI, or SageMaker.
• Proficiency in data processing technologies (Spark, Kafka, Airflow) and data modeling.
• Strong background in deploying models such as APIs or services using Docker, Kubernetes, and REST/gRPC.
• Experience designing data pipelines and integrating AI with production systems.
• Should have an understanding of prompt engineering, LLM fine-tuning, and vector stores (e.g. Pinecone, FAISS, Weaviate).
• Knowledge of cloud AI services (AWS, GCP, Azure) and distributed computing architectures.
• Proven experience implementing observability for models (drift, accuracy, bias, and performance).

Good To Have

• Experience in architecting AI/ML components for low-code/no-code or automation platforms.
• Exposure to GenAI, agentic systems, and conversational AI deployment pipelines.
• Knowledge of compliance frameworks like SOC2, GDPR, and Responsible AI principles.
• Contributions to open-source AI or ML tooling projects.