About bhuvanachandra
I build production-grade AI systems that eliminate repetitive, high-risk manual work by turning complex analysis into reliable, autonomous decision pipelines. As a Machine Learning Engineer specializing in Generative AI, I design agentic, multi-agent architectures and hallucination-resistant RAG systems that replace human-heavy workflows—across insurance claims, private-equity analytics, and commerce automation. I focus on outcomes: systems delivering 10× faster analysis, 98–99% metric accuracy, and lower inference cost and latency, deployed as audit-ready production services on AWS and Databricks. I own the full ML lifecycle—from architecture and data pipelines (Kafka, Delta Lake) to tool-grounded agents, deployment, monitoring, and reliability. My work sits at the intersection of strong ML engineering and business impact: minimizing human touchpoints and shipping AI systems teams can trust in production.
Key Skills
Experience
Consultant Ai / Ml Engineer
CurrentCandata.ai
Built production-grade agentic GenAI systems that replaced analyst-heavy financial workflows, reducing manual effort by ~70–80% and enabling autonomous, audit-ready decision pipelines. Designed hallucination-resistant RAG architectures over investment data (IRR, TVPI, DPI) using schema-aware retrieval, validation agents, and MCP-based context isolation, achieving 98–99% task-level accuracy and eliminating rework from incorrect analyses. Architected multi-agent frameworks (Planner–Executor–Validator) with MCP for structured context handoff, delivering 10× faster analysis and reducing end-to-end decision turnaround from days to minutes. Implemented real-time AI data pipelines using Kafka and Delta Lake, grounding agents in fresh Gold-layer data and improving data freshness from daily batches to near-real-time (<5 min latency). Owned the end-to-end ML lifecycle—architecture, fine-tuning, deployment, monitoring, and reliability—shipping scalable GenAI services on AWS and Databricks, cutting inference costs by ~30–40% through token optimization and agent consolidation
Data Scientist
Tata Consultancy Services
* Built an AI-driven healthcare claims chatbot using OCR (Pytesseract) and FastAPI, reducing end-to-end claims processing time by ~40% while improving structured data accuracy. Automated medical invoice extraction and non-payable item filtering, streamlining insurance workflows and cutting manual effort across claims operations. Led credit card customer segmentation and churn prediction initiatives, enabling targeted card upgrades and EMI offers for high-score customers and driving improved retention and incremental revenue. Developed an NLP-powered Confluence support bot using Hugging Face Transformers and REST APIs, increasing support resolution efficiency by ~30%. Designed and delivered predictive ML models and GenAI POCs using LangChain, Docling, and CrewAI, demonstrating scalable AI solutions for enterprise use. Applied strong ML engineering practices with Python, FastAPI, Docker, and cloud deployments across AWS, GCP, and OCI, translating AI prototypes into measurable business outcomes.
Education
Sree Chaitanya Institute Of Pharmaceutical Sciences
Bachelor Of Technology
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Common Questions
What is bhuvanachandra's expertise?
bhuvanachandra specializes in Fine-Tuning Engineer, with expertise in agentic ai, agentic ai development, dedicated ai clusters, ensemble models, fine tuning techniques.
Where is bhuvanachandra located?
bhuvanachandra is based in hyderabad, telangana, india.
How much experience does bhuvanachandra have?
bhuvanachandra has 5+ years of professional experience.
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