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AI Engineer with 4+ years in Machine Learning, Generative AI & MLOps
AI/ML Engineer and Generative AI Engineer with 4 years of experience delivering production ML and GenAI systems at a global telecom product company, serving enterprise clients across 5 international markets (USA, Gulf, Cambodia, South Africa, Dubai). Designed and shipped a live Two-Tower recommendation system generating 18% incremental revenue uplift, a RAG chatbot ("Aarya") that automated 70% of support workflows saving Rs. 8L/year (company award), and LSTM + Transformer forecasting models driving 54% NRR conversion — collectively powering 1.4M+ daily predictions. Experienced in end-to-end MLOps, prompt engineering, LLMOps, and advanced RAG pipeline design including multimodal document parsing, hybrid retrieval, and cross-encoder reranking. Proficient with GCP; familiar with AWS and Azure cloud services for ML workloads.
Amal Jyothi College of Engineering
Master of Computer Applications (MCA)
January 1, 2022 – January 1, 2022
St. Thomas College
Bachelor of Computer Applications (BCA)
March 1, 2020 – March 1, 2020
6D Technologies
AI / ML Engineer - ML Platform & Generative AI
April 1, 2022 – Present
Bengaluru, Karnataka, India
Advanced RAG Pipeline - Multimodal & Hybrid Retrieval System
June 20, 2026 – Present
End-to-end production-grade GenAI engineering project covering multimodal parsing, hybrid search, reranking, and RAGAS-based evaluation. Implemented semantic and hierarchical chunking using LangChain SemanticChunker and LlamaIndex NodeParser with BGE and OpenAI embeddings stored in FAISS and Qdrant vector databases. Applied page-level indexing enriched with metadata (page number, section title, document type) for precise citation and multi-page context retrieval. Integrated multimodal ingestion via Docling and Unstructured.io to parse PDFs with mixed content (images, tables, text) indexed in a unified retrieval layer. Combined BM25 sparse search and dense embeddings via Reciprocal Rank Fusion (RRF), with cross-encoder reranking (BGE Reranker, Cohere Rerank) — outperforming dense-only retrieval on precision. Evaluated pipeline using RAGAS: Context Precision 0.95, Faithfulness 0.92, Answer Relevance 0.96. Monitored query latency and token usage via Langfuse.
Cultural Fit Analysis
The candidate's experience in a global telecom product company, working across multiple international markets and collaborating with diverse teams (product managers, client stakeholders, data scientists), indicates a strong cultural fit for dynamic, globally-oriented environments. Their proactive approach to adopting open-source tooling, leading platform overhauls, and mentoring junior staff suggests a collaborative and growth-oriented mindset. The diversity of projects, from recommendation systems to generative AI chatbots, shows adaptability and a broad interest in applying AI to various business problems.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through complex system design (e.g., two-stage recommendation systems, hybrid RAG). Their experience in leading projects, mentoring junior engineers, and authoring documentation indicates good communication and collaboration skills. The focus on cost reduction and efficiency improvements aligns with operational excellence. The candidate's ability to work across international markets and with diverse stakeholders suggests adaptability and a strong work ethic.