Ramu Ganta
AI/ML Engineer | Generative AI & Production ML Systems
LinkedIn
About Me
Transforming Business Through AI Innovation
AI/ML Engineer with 8+ years building production Generative AI systems, RAG architectures, and end-to-end ML pipelines across enterprise, manufacturing, and CRM environments. Deep expertise deploying LLM-powered applications using AWS Bedrock, Azure OpenAI, LangChain, and vector databases.
Track record of delivering measurable impact: 60% faster information retrieval, 30%+ accuracy improvements, and successful enterprise AI adoption across Fortune 500 organizations. Passionate about bridging the gap between cutting-edge AI research and practical business solutions.
Quick Facts
  • 8+ years in AI/ML engineering
  • 15+ production models deployed
  • 500+ users enabled with AI tools
  • San Francisco, CA (Remote/Relocation)
Core Expertise
Generative AI & LLMs
AWS Bedrock, Azure OpenAI, Claude, GPT-4, LangChain, LlamaIndex, Prompt Engineering, Fine-Tuning
RAG & Vector Search
Retrieval-Augmented Generation, Pinecone, FAISS, Semantic Search, Embeddings, Agentic Workflows
Machine Learning
Time-Series Forecasting, Anomaly Detection, XGBoost, LightGBM, Feature Engineering, Model Evaluation
MLOps & Deployment
MLflow, Docker, Kubernetes, FastAPI, CI/CD, Airflow, Model Monitoring, A/B Testing
Key Achievements & Impact
60%
Faster Retrieval
Reduced knowledge discovery time with enterprise RAG system
30-45%
Accuracy Boost
Improved forecasting and planning accuracy across models
500+
Users Enabled
Self-service AI platform adoption across enterprise
15+
Production Models
Deployed with high availability and monitoring
Experience
AI/ML Engineer at AUConnects LLC
Generative AI & MLOps | August 2025 – Present
Architected enterprise GenAI RAG platform with multi-stage pipeline processing 50K+ documents, reducing information retrieval time by ~60% for operational and financial teams. Orchestrated LLM workflows using AWS Bedrock and Azure OpenAI with advanced prompt engineering and tool calling.
Built production FastAPI microservices for real-time GenAI inference with async processing and monitoring, deployed on AWS ECS with auto-scaling. Established comprehensive MLOps practices using MLflow for experiment tracking and automated CI/CD pipelines.
Key Technologies
  • AWS Bedrock (Claude 3.5)
  • Azure OpenAI (GPT-4)
  • LangChain & Pinecone
  • FastAPI & Docker
  • MLflow & GitHub Actions
  • Azure Synapse & Delta Lake
Enterprise RAG Platform Architecture
Document Processing
50K+ documents ingested with intelligent chunking and metadata extraction
Embeddings & Indexing
Sentence Transformers with Pinecone vector database for semantic search
Hybrid Retrieval
Multi-stage retrieval with metadata filtering and Cohere re-ranking
LLM Synthesis
Claude 3.5 and GPT-4 for contextual response generation
This multi-stage pipeline enables self-service AI for 500+ users, dramatically reducing time spent searching for information and enabling faster decision-making across the organization.
AI/ML Engineer at Carrier HVAC
Predictive Analytics & MLOps | January 2024 – August 2025
Ensemble Forecasting Models
Designed and deployed XGBoost, LightGBM, and Prophet models for demand planning and cost variance prediction, improving planning accuracy by ~30% using Databricks and MLflow.
Production ML Pipelines
Built automated pipelines on Azure Synapse and Databricks with feature engineering and model validation, reducing refresh cycles from 24 hours to under 1 hour.
Anomaly Detection Systems
Developed Isolation Forest and LSTM models identifying supply chain disruptions and operational deviations, enabling earlier intervention and risk mitigation.
Performance Optimization
Optimized training using Delta Lake partitioning and vectorized processing, reducing model training time by ~40% and enabling more frequent updates.
Machine Learning Engineer at Sabnext Solutions
CRM Analytics | June 2023 – December 2023
Built gradient boosting models (XGBoost, CatBoost) for churn prediction, pipeline conversion, and revenue forecasting achieving ~30% improvement in accuracy. Developed customer segmentation models using RFM analysis and K-means clustering to drive targeted sales campaigns.
Implemented anomaly detection combining Isolation Forest models with business rules for CRM data quality monitoring. Automated ETL pipelines using Apache Airflow achieving 95%+ on-time execution. Delivered FastAPI scoring services integrated with Salesforce providing real-time churn risk scores.
Earlier Experience & Foundation
1
Vassarlabs IT Solutions (QCode) | 2022-2023
Built time-series forecasting models using Prophet, ARIMA, and LSTM for supply chain and financial planning. Developed anomaly detection pipelines for supplier lead-time monitoring enabling earlier risk identification.
2
Vassarlabs IT Solutions | 2017-2021
Built Python automation framework for financial reconciliation achieving 99% data accuracy. Implemented ML models (Random Forest, XGBoost, SVM) for classification achieving 85%+ accuracy. Developed ETL workflows and BI dashboards reducing manual reporting time by 35%.
Featured Projects
AI Interview & Feedback Tool
Production GenAI Application
Built AI interview simulator using Streamlit and LLM backend (OpenAI/AWS Bedrock) conducting mock technical interviews and generating structured evaluation reports with scoring and personalized feedback.
Implemented multi-stage workflow with state management: interview setup → conversational Q&A → performance evaluation → feedback generation. Enables candidates to practice technical interviews with realistic AI-powered scenarios.

Technologies: Streamlit, OpenAI API, AWS Bedrock, LangChain, Python
LLM Application Portfolio
30+ End-to-End GenAI Applications
RAG Systems
Advanced retrieval-augmented generation with query decomposition and multi-query retrieval
Agentic Workflows
Multi-agent systems with specialized roles and orchestration patterns
Document Q&A
Intelligent document understanding with semantic search and context-aware responses
Code Generation
AI-powered code synthesis with tool calling and execution capabilities
Comprehensive portfolio demonstrating mastery of modern LLM techniques including advanced RAG patterns, evaluation frameworks, and production deployment strategies.
Enterprise RAG & Multi-Agent System
Central Orchestrator
LangGraph coordination of specialized agents
Retriever Agent
Optimized document search with hybrid retrieval
Synthesizer Agent
Context-aware response generation
Quality Assessor
Response validation and accuracy checking
Vector Store
Pinecone/FAISS with metadata enrichment
Designed multi-agent RAG framework with specialized agents using LangGraph for orchestration. Indexed large document collections with optimized chunking, embeddings generation, and metadata enrichment enabling hybrid search and improved retrieval accuracy.
Education
Academic Excellence
Master of Science in Data Science
Wichita State University, Kansas, USA
GPA: 3.84/4.0 | May 2023
Advanced coursework in machine learning, deep learning, statistical modeling, and big data analytics. Specialized in AI/ML engineering and production systems.
Bachelor of Technology in Computer Science
Birla Institute of Technology, India
GPA: 3.67/4.0 | May 2018
Strong foundation in algorithms, data structures, software engineering, and computer systems. Focus on AI and machine learning applications.
Professional Certifications
AWS Certified Machine Learning – Specialty
Advanced AWS ML services including SageMaker, Bedrock, and production deployment strategies
Microsoft Azure AI Engineer Associate (AI-102)
Azure OpenAI, Cognitive Services, and enterprise AI solution architecture
Google Cloud Professional ML Engineer
GCP ML services, Vertex AI, and scalable ML pipeline development
Databricks Certified ML Professional
Advanced MLOps, Delta Lake, and distributed ML training on Databricks
LLM Engineering Specialization
OpenAI, LangChain, Vector Databases, Fine-Tuning (365 Data Science)
Model Context Protocol (MCP)
Advanced AI system integration and context management (365 Data Science)
Technical Skills Deep Dive
Generative AI & LLMs
  • AWS Bedrock (Claude, Titan)
  • Azure OpenAI (GPT-4, GPT-3.5)
  • Anthropic Claude
  • Hugging Face Transformers
  • LangChain & LlamaIndex
  • Prompt Engineering & Few-Shot Learning
  • Fine-Tuning (LoRA, QLoRA)
RAG & Vector Search
  • Retrieval-Augmented Generation
  • Embeddings & Semantic Search
  • Pinecone & FAISS
  • Chroma & Weaviate
  • Re-ranking & Hybrid Search
  • Agentic Workflows & LangGraph
Machine Learning
  • Time-Series: Prophet, ARIMA, LSTM
  • Anomaly Detection
  • XGBoost, LightGBM, CatBoost
  • Random Forest, SVM
  • Feature Engineering
  • Model Evaluation & Validation
MLOps & Cloud
  • MLflow, Docker, Kubernetes
  • FastAPI, CI/CD, Airflow
  • AWS (Bedrock, SageMaker, Lambda)
  • Azure (OpenAI, ML, Synapse)
  • GCP (BigQuery, Vertex AI)
  • Model Monitoring & Drift Detection
Impact Across Industries
Enterprise Technology
Built GenAI RAG platform enabling 500+ users with self-service AI, reducing knowledge discovery time by 60% and transforming how teams access information.
Manufacturing & HVAC
Deployed forecasting models improving planning accuracy by 30%, with anomaly detection systems enabling early identification of supply chain disruptions.
CRM & Sales
Delivered churn prediction and customer segmentation models driving targeted campaigns, with real-time scoring integrated into Salesforce workflows.
MLOps & Production Excellence
01
Experiment Tracking
MLflow for versioning, metrics logging, and model registry with comprehensive experiment management
02
Automated Pipelines
CI/CD with GitHub Actions, automated testing, and approval workflows for production promotion
03
Containerization
Docker and Kubernetes for reproducible deployments with auto-scaling and high availability
04
Monitoring & Alerting
Evidently AI and CloudWatch for drift detection, performance tracking, and automated alerting
05
Model Governance
Version control, documentation, and compliance frameworks for enterprise AI systems
Established comprehensive MLOps practices supporting 15+ production models with consistent performance, automated monitoring, and rapid iteration cycles.
Data Engineering & Optimization
Pipeline Optimization
Optimized Azure Synapse and Databricks pipelines using Delta Lake, partitioning strategies, and query optimization, reducing refresh cycles from 12-24 hours to under 1 hour.
Implemented vectorized processing and efficient data structures reducing model training time by 40% and enabling more frequent model updates.
Technologies
  • Python, SQL, PySpark
  • Databricks & Delta Lake
  • Azure Synapse Analytics
  • Apache Airflow
  • ETL/ELT Pipelines
Leadership & Collaboration
Cross-Functional Partnership
Partnered with product, engineering, and business teams to translate requirements into scalable AI solutions, driving adoption across 10+ teams and ensuring alignment with business objectives.
Technical Mentorship
Guided junior engineers on ML best practices, code reviews, and architecture decisions. Established standards for model development, testing, and deployment across the organization.
Stakeholder Communication
Translated complex technical concepts for non-technical audiences, delivered executive presentations, and built trust through transparent reporting on model performance and business impact.
Let's Build the Future Together
I'm passionate about leveraging cutting-edge AI technology to solve real business problems. With 8+ years of experience building production GenAI systems, RAG architectures, and end-to-end ML pipelines, I bring both technical depth and business acumen to every project.
Whether you're looking to implement enterprise AI solutions, optimize ML operations, or explore innovative applications of LLMs and generative AI, I'd love to connect and discuss how we can create impact together.
Contact Information
Phone: 316-372-6764
Location: San Francisco, CA
(Open to Remote & Relocation)