Principal AI Architect

The core requirements for the job include the following :

Core AI/ML Skills :

- Advanced machine learning and deep learning architectures.

- Time-series and foundational forecasting models.

- Demand forecasting, inventory forecasting, and retail planning models.

- Model evaluation, benchmarking, drift detection, and monitoring.

- Scalable ML system design and MLOps.

Retail AI Domain Skills :

- Retail demand planning, assortment, pricing, promotions, and inventory optimisation.

- Domain adaptation for retail-specific AI use cases.

- Understanding of retail data : POS, inventory, promotions, pricing, loyalty, supply chain.

- Forecast explainability and business-impact measurement.

LLM / GenAI Skills :

- LLM fine-tuning and domain adaptation.

- Supervised Fine-Tuning, SFT.

- RLHF / RLAIF workflows.

- Prompt engineering and prompt evaluation.

- Retrieval-Augmented Generation (RAG).

- Agentic AI architecture.

- Guardrails, hallucination reduction, and LLM safety.

Architecture Skills :

- End-to-end AI platform architecture.

- Cloud-native AI systems on AWS, Azure, or GCP.

- Distributed training and inference optimisation.

- Vector databases and semantic search.

- Model serving, latency optimisation, and cost optimisation.

- Data lakehouse/feature store architecture.

Leadership Skills :

- AI strategy and roadmap ownership.

- Technical leadership for senior ML engineers and data scientists.

- Cross-functional collaboration with product, engineering, and business teams.

- Executive communication and solution storytelling.

- Architecture governance and best-practice definition.

Preferred Tools / Technologies :

- Python, PyTorch, TensorFlow.

- Hugging Face, LangChain, LlamaIndex.

- MLflow, Kubeflow, Airflow.

- Databricks, Snowflake, Spark.

- Kubernetes, Docker.

- OpenAI, Anthropic, Gemini, open-source LLMs.

- Vector DBs : Pinecone, Weaviate, FAISS, Milvus.

Good to Have :

- 12+ years in AI/ML, data science, or enterprise architecture and 5+ years architecting production ML systems.

- Hands-on experience with forecasting and GenAI/LLM systems.

- Knowledge of the retail, CPG, supply chain, or commerce domain.

- Proven record of taking AI products from prototype to production.