Posted on: 24/06/2026
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.
The impact that you will be making :
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.
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.
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