About the role
Required 5-10years Hands-on Architect to design and scale enterprise insight platforms powered by modern data, AI, and Generative AI ecosystems. This role focuses on enabling self-service analytics and intelligent decision-making using platforms such as Dataiku (on EKS), Posit, and AWS SageMaker, while embedding GenAI-driven insight capabilities across the data lifecycle.
You will bridge data engineering, data science, and business domains to deliver scalable, governed, and AI-powered insight solutions.
Desired skill:
- Strong experience in data/AI architecture, insight enablement, or analytics platforms
- Hands-on expertise with:
- Dataiku (including EKS deployment)
- Posit (RStudio/Workbench)
- AWS SageMaker (training, deployment, pipelines)
- Strong understanding of:
- Modern data architectures (Datamesh, lakehouse, semantic layers, data products)
- GenAI patterns (RAG, agents, prompt engineering)
- MLOps/DataOps practices
- Proficiency in Python and/or R
Key Responsibilities
Platform & Architecture
- Architect and design AI &ML ecosystems leveraging Dataiku (on Kubernetes / EKS), Posit, and SageMaker.
- Define cloud-native architectures on AWS integrating EKS, S3, Lambda, API Gateway, and SageMaker for analytics and AI workloads.
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GenAI Enablement & Patterns
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Self-Service & Insight Delivery
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Governance, MLOps & Observability
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Collaboration & Enablement
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Tools, Libraries & Frameworks
- Enable and standardize usage of:
- LangChain, LangGraph, LlamaIndex for orchestration and agent design
- Hugging Face Transformers for model integration and experimentation
- Vector databases (FAISS, OpenSearch, Pinecone, Chroma)
- ML/AI frameworks (PyTorch, TensorFlow, scikit-learn)
- Integrate GenAI workflows into:
- Dataiku pipelines (recipes, plugins, scenarios)
- Posit notebooks and analytical workflows
- SageMaker pipelines and endpoints