Experience:

• 12+ years of total experience
• 5+ years of AI/ML, data science, or enterprise architecture experience
• Experience architecting production ML systems

Responsibilities:

• Advanced machine learning and deep learning architecture
• Design and development of time-series and foundational forecasting models
• Demand forecasting, inventory forecasting, and retail planning
• Model evaluation, benchmarking, drift detection, and monitoring
• Scalable ML system design and MLOps
• LLM fine-tuning and domain adaptation
• Supervised Fine-Tuning (SFT) and RLHF / RLAIF workflows
• Prompt engineering and evaluation
• Retrieval-Augmented Generation (RAG)
• Agentic AI architecture design
• Implementing guardrails, hallucination reduction, and LLM safety
• End-to-end AI platform architecture on cloud-native systems
• Distributed training and inference optimization
• Vector databases and semantic search integration
• Model serving with latency and cost optimization
• Data lakehouse and feature store architecture
• Technical leadership for senior ML engineers and data scientists
• Cross-functional collaboration with product, engineering, and business teams
• AI strategy and roadmap ownership
• Architecture governance and best-practice definition
• Executive communication and solution storytelling

Additional responsibilities:

• Collaborate with product and engineering teams to shape enterprise software
• Lead large, high-impact projects for global Fortune 500 clients

More skills:

AI/ML, Advanced machine learning, Deep learning architecture, Time-series forecasting, Foundational forecasting models, Demand forecasting, Inventory forecasting, Retail planning models, Model evaluation, Benchmarking, Drift detection, Monitoring, Scalable ML system design, MLOps, LLM fine-tuning, Domain adaptation, Supervised Fine-Tuning (SFT), RLHF / RLAIF workflows, Prompt engineering, Prompt evaluation, Retrieval-Augmented Generation (RAG), Agentic AI architecture, Guardrails, Hallucination reduction, LLM safety, End-to-end AI platform architecture, Cloud-native AI systems, AWS, Azure, GCP, Distributed training, Inference optimization, Vector databases, Semantic search, Model serving, Latency optimization, Cost optimization, Data lakehouse architecture, Feature store architecture, Snowflake, Spark, Kubernetes, Docker, Gemini, Open-source LLMs, Vector DBs: Pinecone, Weaviate, FAISS, Milvus

Other:

• Full-Time role
• Work in a high-growth startup environment with a flat, collegial culture
• Best-in-class remuneration and benefits
• Platform for personal and professional growth with ownership and autonomy
• Company accolades including rankings by Financial Times, Inc. 5000, RIS Software Leader Board, Fortune, and Gartner reports
• Strategic partnership with Google for generative AI tools