Design AI-ready data foundations including document ingestion, chunking, embeddings, vector search, hybrid search, re-ranking, knowledge graphs, metadata schemas, data lineage, source attribution, and access-aware retrieval.
Define and implement LLMOps / MLOps practices including model registry, prompt and pipeline versioning, evaluation frameworks, observability, monitoring of latency/quality/cost, model drift detection, retraining triggers, and deployment controls across DEV/UAT/PROD environments.
Design AI-ready data foundations for RAG and agentic AI, including document ingestion, chunking strategies, embedding pipelines, vector databases, hybrid search, re-ranking, metadata schemas, knowledge graph design, source attribution, data lineage, freshness controls, and access-aware retrieval.
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Stay updated with the latest advancements in LLMs, Generative AI, and machine learning technologies
Demonstrated passion for Generative AI and its application to real-world business problems; AI related hobby projects with strong developer background also counts
Experience with Python programming and scripting
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Systems Architecture: Familiarity with LLVM-level compilation, Linux system scheduling, or FPGA firmware development.
Job Summary
We are seeking a highly motivated Quantum-HPC Software Systems Engineer / Research Scientist to develop next-generation Hybrid Quantum-Classical Computing (HQCC) infrastructure.
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