AI Architect

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.

Skills

AI, Airflow, Databricks, GenAI, Kubeflow, LLM, ML, MLOps, MLflow, architecture, data science, large language model, machine learning