About the role
Requirements:
- 12+ years of total Experience, 5+ years of AI/ML, data science, or enterprise architecture and architecting production ML systems.
- Hands-on experience with forecasting and GenAI/LLM systems.
- Proven record of taking AI products from prototype to production.
- 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.
- 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.
Core AI/ML Skills:
- Advanced machine learning and deep learning architecture.
- 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.
Architecture Skills:
- End-to-end AI platform architecture.
- Cloud-native AI systems on AWS, Azure, or GCP.
- Distributed training and inference optimization.
- Vector databases and semantic search.
- Model serving, latency optimization, and cost optimization.
- 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.