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Kartikeya Anand
Mid-level Machine Learning Engineer specializing in NLP, LLMs, and multimodal modeling
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About
Built and productionized a telecom-focused RAG assistant by LoRA fine-tuning LLaMA-2 and integrating LangChain+FAISS behind a FastAPI service, with dashboards and a human feedback UI for engineers. Demonstrated measurable impact (≈40% faster document lookup, +8–10% retrieval precision) and strong MLOps rigor via Airflow orchestration, CI/CD, and monitoring for drift and failures.
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Key Strengths
- Built and deployed a production RAG system for telecom/regulatory documents (3GPP, network design specs)
- Improved engineer document lookup time ~40%
- Improved retrieval precision by ~8–10% using hybrid retrieval and tuning
- Strong evaluation discipline (gold set of 150 real queries; Precision@k, ROUGE-L, semantic similarity)
- Performance optimization focus (managed retrieval accuracy/latency; 8-bit quantization reduced inference cost ~25%)
- End-to-end ML/NLP pipeline orchestration with Airflow (extraction → cleaning → embeddings → training → evaluation)
- Practical MLOps approach (Docker, CI/CD, monitoring drift/failures, human feedback loops)
- Effective data preprocessing for large unstructured specs (regex + spaCy standardization)
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Publications
1 publication
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