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Vishnuvardhan Reddy Jakku

Senior AI/ML Engineer • Kansas City, MO • V****************@gmail.com • 913****617 • linkedin.com/••••• • drivetube.ai/•••••

Professional Summary

Senior AI/ML Engineer with 6+ years of experience designing and delivering production-grade Generative AI, LLM, and machine learning systems. Strong hands-on expertise in LangChain, Hugging Face Transformers and Pinecone for Retrieval-Augmented Generation, plus model fine-tuning with LoRA/PEFT and LlamaIndex for contextual grounding. Experienced building cloud-native MLOps and LLMOps pipelines using MLflow, TensorBoard, Kubeflow, Terraform and AWS services (Bedrock, Lambda, S3) to automate retraining, A/B and shadow deployments, and drift detection. Worked across healthcare, insurance, telecom and CPG/retail domains to deploy secure, explainable AI (SHAP/LIME) and real-time inference pipelines using PyTorch, TensorFlow and ONNX. Proven at taking prototypes to production, improving model reliability and reducing inference latency while controlling cloud costs and adoption risk.

Technical Skills

Programming Language: Python,SQL
Databases: Amazon DynamoDB,Azure SQL Database
Cloud Platforms: TensorBoard,Amazon Web Services,Lambda,S3
DevOps & Infrastructure: Terraform,GitHub Actions
Messaging & Monitoring: Apache Kafka,Kibana,CloudWatch
Data Engineering & Processing: Azure Databricks
Data Analysis & Visualization: Tableau,Power BI
Machine Learning & AI: Word2Vec
AI/ML Frameworks & Libraries: Hugging Face Transformers,PyTorch,TensorFlow,LightGBM,ONNX
Generative AI & LLMs: LangChain,LlamaIndex,OpenAI API,Bedrock,LoRA,PEFT,Whisper,TF-IDF
Vector Databases & RAG: Pinecone
MLOps: MLflow,Kubeflow

Work Experience

Charter Communications
MO, USA
Sr. AI/ML Engineer
Sep 2024 – Present
Telecommunications firm — developed enterprise generative-AI and prediction systems to support internal Q&A, policy summarization, and real-time customer analytics.
Tech Stack: LangChain, LlamaIndex, Pinecone, Amazon Bedrock, Amazon SQS, Hugging Face Transformers, BERT, GPT-2, LoRA, PEFT, Kubeflow, MLflow, TensorBoard, ONNX, LightGBM, PyTorch
  • Architected and deployed a Retrieval-Augmented Generation (RAG) system using LangChain and LlamaIndex with Pinecone vector search for internal enterprise Q&A, achieving 92% answer accuracy and reducing hallucinated responses.
  • Orchestrated continuous feedback and infra provisioning using Amazon SQS, Amazon Bedrock, Step Functions and Terraform to capture runtime signals and provision reproducible LLM inference pipelines.
  • Implemented an abstractive summarization pipeline using Hugging Face Transformers to automate policy-document summarization and reduce manual review workload through human-in-the-loop validation.
  • Fine-tuned domain-specific models (BERT, GPT-2) with LoRA/PEFT and managed distributed training runs on Kubeflow to improve intent recognition and reduce fine-tuning compute cost.
  • Automated model retraining and observability with MLflow and TensorBoard; instrumented drift detection triggers and automated rollback policies via AWS Lambda.
  • Optimized real-time inference by converting LightGBM and PyTorch models to ONNX and enabling GPU-accelerated inference, improving latency 2.5x for critical prediction endpoints.
Humana
KY, USA
AI/ML Engineer
May 2023 – Aug 2024
Health insurance — built LLM-based assistants, transcription and secure document QA interfaces to speed contract review and compliance workflows.
Tech Stack: LangChain, OpenAI API, Whisper, LoRA, Pinecone, Streamlit, Docker, AWS Lambda, DynamoDB, GitHub Actions, TeamCity, UDeploy, CloudWatch
  • Built an LLM-powered contract assistant with LangChain and OpenAI API to extract clauses, obligations and renewal terms, delivering 90%+ extraction accuracy on production documents.
  • Implemented a Whisper-based audio transcription and summarization pipeline to process call-center recordings and accelerate compliance review through automated summaries.
  • Applied LoRA fine-tuning to adapt base LLMs to insurance-specific language under constrained compute budgets, improving downstream extraction robustness.
  • Developed a secure RAG document QA interface using Pinecone for contextual retrieval and Streamlit for a controlled user surface with metadata-based grounding.
  • Packaged inference workloads as Docker container images and deployed serverless endpoints using AWS Lambda and S3 with DynamoDB for session persistence.
  • Standardized CI/CD and monitoring using GitHub Actions, TeamCity, UDeploy and CloudWatch to improve deployment reliability and reduce cloud spend.
Sunglow CNC
Hyderabad, India
Machine Learning Engineer
Sep 2020 – Dec 2022
CPG and retail analytics consultancy — delivered forecasting, uplift measurement and recommendation systems for client-facing solutions.
Tech Stack: TensorFlow, PyTorch, Azure Databricks, TF-IDF, Word2Vec, LSTM, Apache Kafka, Hive, Kibana
  • Conducted causal ML experiments and uplift modeling for CPG/retail clients using causal inference frameworks and revenue-forecast techniques, improving uplift measurement accuracy 15%.
  • Implemented NLP-based text analytics (TF-IDF, Word2Vec, LSTM) to derive customer sentiment and product feedback signals for merchandizing decisions.
  • Built demand-forecasting and churn-prediction models with TensorFlow and PyTorch on Azure Databricks, improving retraining throughput and model stability for batch workloads.
  • Developed deep-learning recommendation models to support personalized marketing campaigns that contributed to observable retention gains.
  • Engineered real-time streaming ingestion and ETL pipelines with Apache Kafka and Hive to enable near-real-time analytics and feature generation for models.
  • Investigated production logs and dashboards in Kibana and optimized MapReduce and feature-extraction pipelines to shorten failure recovery cycles.

Education

University of Central Missouri
Master's in Computer Science

Certifications

Google Cloud Platform (GCP) Certified
AZ-900: Microsoft Azure Fundamentals — Microsoft

Achievements

  • Enterprise RAG Q&A accuracy improvement: Achieved 92% answer accuracy on an enterprise RAG Q&A system (LangChain + Pinecone), reducing hallucinated responses.
  • Generative summarization reduced review workload: Shortened manual document review time by building a Hugging Face Transformers summarization pipeline with human-in-the-loop validation.
  • High-accuracy contract extraction: Delivered 90%+ extraction accuracy for an LLM-powered contract assistant, greatly reducing manual review effort.
  • Call-center compliance automation: Trimmed compliance review effort by automating transcription and summarization of call-center audio using Whisper pipelines.
  • Model latency and inference optimization: Improved inference latency 2.5x for real-time models by converting to ONNX and enabling GPU-accelerated inference.
  • Cloud cost and release-velocity improvements: Reduced cloud infrastructure costs and improved release velocity through CI/CD improvements and compute/storage optimizations.

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