Machine Learning Engineer

Summary

Machine Learning Engineer with *+ years of experience designing, implementing, and deploying AI/ML solutions in enterprise environments. Proven expertise in generative NLP, large language models (LLMs), and neural networks, with a track record of improving model accuracy by over 25% and reducing deployment times through MLOps pipelines and cloud services integration. Skilled in data science, optimization modeling, and building scalable model architectures for high-impact business and research applications. Adept at collaborating with cross-functional teams to deliver secure, efficient, and mission-critical solutions aligned with organizational goals.

Experience

SIPTEK Columbus, OH

Machine Learning Engineer 02/2024 - 09/2025

Built a SaaS personalization platform for an e-commerce client to improve CTR and reduce time-to-result by delivering tailored product recommendations.

Designed and implemented ETL pipelines to process raw PostgreSQL logs into clean user–item interaction datasets for analytics.

Performed feature engineering on user, item, and context data; experimented with ALS, XGBoost, and LightGBM to optimize recommendation accuracy.

Deployed trained models as FastAPI microservices in Docker, integrated with the frontend via REST APIs, and scaled deployments using Kubernetes.

Improved system latency by precomputing top recommendations, monitored performance with Prometheus & Grafana, and ran A/B tests for model evaluation.

Automated model retraining and CI/CD pipelines, enabling continuous experimentation and seamless deployment to staging and production environments.

Skills

Data Engineering Tools:, Machine Learning & AI Frameworks:, AWS (Expert), ETL Pipelines (Expert), PostgreSQL, Opensearch, Pinecone, Scikit-learn, TensorFlow, PyTorch (Expert), ML Libraries (Expert), Tableau (Expert), Excel, Ruby, DevOps(Docker, Kubernets, CI/CD), AWS SageMaker, AWS bedrock Education

St Francis College New York

INFORMATION TECHNOLOGY 08/2025

SRM Institute of Science and Technology

COMPUTER SCIENCE WITH AI & ML

MY Portfolio

https://naveenflix.vercel.app/

Certificates

AWS Certified AI Practitioner, Machine Learning & Big Data With Python & R ( Prag Robotics) Projects

Tire Recommendation Chatbot (Demo Project, Python & LLMs) Developed a chatbot for Discount Tire by fine-tuning a LLaMA model on a custom tire dataset to provide personalized tire recommendations.

Designed and implemented a frontend interface to deliver a seamless, user-friendly interaction experience. NAVEEN KANCHARLA

513-***-**** *********@*****.*** Phoenix, AZ

Built and cleaned a domain-specific dataset of tire attributes and customer queries to enhance recommendation accuracy.

Integrated LLM fine-tuning, API design, and frontend development, ensuring smooth end-to-end functionality. Demonstrated strong applied AI skills by combining NLP, model training, and full-stack integration for a real-world retail use case.

Multi-Document RAG Chatbot (Personal Project, Python) Developed a retrieval-augmented generation (RAG) chatbot to handle multiple documents, integrating OpenAI APIs to reduce query latency by 35%.

Designed and implemented end-to-end software development: design, coding, code review, testing, bug fixing, and API documentation.

Optimized deployment using Docker, enabling scalability for large datasets. Collaborated via GitHub issues, providing feedback to improve customer-focused features and engineering excellence. AI Code Reviewer (Personal Project, Python)

Built an AI-powered tool to automate code reviews, identifying bugs 30% faster through Python-based analysis and debugging.

Integrated DevOps practices for SaaS deployment with focus on reliability and maintainability. Contributed across the SDLC (design, coding, testing, documentation), ensuring quality and customer-focused outcomes.

Promoted collaboration and knowledge-sharing through thoughtful feedback mechanisms. Image-to-Text Agent (Personal Project, Python)

Implemented an agent for OCR and NLP, using OpenAI Vision APIs to achieve 90% text extraction accuracy while reducing processing time by 25%.

Focused on maintainable code, debugging, and DevOps deployment in a simulated SaaS environment. Worked independently and collaboratively, providing clear communication and feedback to enhance features. Sentiment Analysis App (Personal Project, Python)

Designed a real-time sentiment analysis app with ML models, delivering 85% classification accuracy. Involved in end-to-end development: coding, testing, bug fixing, code review, and documentation. Integrated DevOps workflows for efficient SaaS deployment and scaling. Collaborated with open-source contributors to refine features and improve reliability.

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