AI/ML Engineer with 8+ years of experience building and deploying machine learning and generative AI systems in production on AWS. Past two years focused on GenAI - RAG pipelines, multi-agent workflows with LangGraph, and LLM integration using AWS Bedrock and OpenAI. Built systems processing 50K+ documents, improved forecasting accuracy by 30-45%, and deployed MCP-based connectors for secure agent-to-data access. Hands-on across the full AI lifecycle - experiment tracking (MLflow), containerized deployment (Docker, ECS), and production monitoring (CloudWatch). AWS certified with deep experience across SageMaker, Bedrock, ECS, Lambda, API Gateway, and IAM.
AWS Bedrock (Claude, Titan), OpenAI GPT-4, Anthropic Claude, Hugging Face Transformers, LangChain, LlamaIndex, Prompt Engineering, Tool Calling, Guardrails, RAGAS/DeepEval
RAG pipelines, Semantic Search, Embeddings (OpenAI, Titan, HuggingFace), Pinecone, FAISS, Chroma, Hybrid Search, Re-Ranking, Knowledge Graphs (Neo4j, KG-RAG)
Multi-Agent Systems, LangGraph, MCP, Stateful Workflows, Conditional Routing, Memory Management, Human-in-the-Loop, ReAct/ReWOO
XGBoost, LightGBM, Prophet, ARIMA, LSTM, Anomaly Detection, Classification, Clustering, NLP, Feature Engineering
SageMaker, ECS, Kubernetes, Lambda, Docker, ECR, MLflow, FastAPI, CI/CD (GitHub Actions), Drift Detection, A/B Testing, LangSmith
Python, SQL, PySpark, Databricks, Delta Lake, Snowflake, Airflow | AWS (6+ years): SageMaker, Bedrock, ECS, EC2, S3, Lambda, API Gateway, IAM, CloudWatch | GCP: BigQuery
Cut information retrieval time with enterprise RAG platform over 50K+ documents
Improved demand and cost planning accuracy in supply chain with ensemble models
LLM-powered document analysis tools at Carrier HVAC reduced manual review effort
Enterprise RAG platform ingesting and retrieving from large document corpus
Worked cross-functionally to operationalize AI across product, architecture, and business teams
Building and deploying production ML and GenAI systems on AWS
Architected and deployed an enterprise RAG platform processing 50K+ documents using LangChain, Pinecone, and FAISS - reduced retrieval time by ~60% for operations and finance teams
Designed agentic AI workflows using LangGraph with multi-step reasoning, task delegation, tool invocation, and response validation
Deployed LLM-powered agent workflows using AWS Bedrock (Claude) and OpenAI GPT-4 with tool-calling, structured outputs, and safety guardrails
Built RAG pipelines using OpenAI and Titan embeddings with chunking strategies, re-ranking, and metadata enrichment
Integrated Neo4j knowledge graphs for hybrid KG + vector retrieval (KG-RAG) to improve factual consistency
Designed serverless GenAI APIs using AWS Lambda and API Gateway for low-latency internal tooling
Implemented blue-green deployment patterns for containerized AI services on ECS with autoscaling policies
Designed MCP-based connectors and tool schemas enabling agents to securely invoke APIs without exposing credentials
Containerized services with Docker and set up CI/CD pipelines using GitHub Actions and Amazon ECR
AWS Bedrock, SageMaker, ECS, Lambda, API Gateway, CloudWatch, IAM, ECR, LangChain, LangGraph, MCP, Pinecone, FAISS, Neo4j, MLflow, FastAPI, Docker, Databricks, Airflow, GitHub Actions
50K+ docs, PDF parsing, recursive chunking, metadata enrichment
OpenAI & Titan embeddings, Pinecone + FAISS, hybrid KG-RAG with Neo4j
Semantic search, hybrid search, re-ranking for factual consistency
AWS Bedrock (Claude), OpenAI GPT-4, LangChain, tool-calling, guardrails
LangGraph multi-agent, MCP connectors, stateful reasoning, human-in-the-loop
ECS blue-green deploy, Lambda APIs, CloudWatch monitoring, CI/CD
This enterprise pipeline powers self-service AI at AUConnects, combining retrieval, orchestration, and agentic automation to reduce information search time and support faster decision-making across the organization.
Led development using AWS Bedrock (Claude) and LangChain - operations teams could query technical manuals and SOPs in natural language, reducing manual review by ~40%
Built RAG pipeline over internal documentation using Titan embeddings, FAISS, and LangChain with recursive chunking and metadata filtering
Developed prompt engineering workflows for structured extraction from supplier reports and warranty claims
Integrated LLM-based summarization into PowerBI dashboards via FastAPI endpoints for AI-generated insights
Built and deployed XGBoost, LightGBM, Prophet models improving demand planning accuracy by ~30-45%
Implemented anomaly detection pipelines; operationalized ML models using Databricks, MLflow, and Docker with version-controlled deployment workflows
Technologies: Python, XGBoost, LightGBM, AWS Bedrock, SageMaker, S3, EC2, IAM, LangChain, FAISS, Databricks, MLflow, Docker, Delta Lake, FastAPI, PowerBI
Technologies: Python, XGBoost, CatBoost, Scikit-learn, SQL, FastAPI, Docker, AWS EC2, S3, CloudWatch, Airflow, Salesforce API
Vassarlabs IT Solutions (QCode Software), Remote, USA | January 2022 – May 2023
Technologies: Python, Prophet, ARIMA, LSTM, TensorFlow, Scikit-learn, SQL, Docker, AWS EC2, S3, Power BI
Vassarlabs IT Solutions, Hyderabad, India | May 2017 – December 2021
Technologies: Python, Scikit-learn, Pandas, SQL, Flask, AWS EC2, S3, Power BI, Tableau
Built a RAG system over CDC public health documents with PDF parsing, recursive chunking, FAISS vector retrieval, and hybrid search. Deployed on AWS with Streamlit frontend.
Tech: LangChain, OpenAI, FAISS, Streamlit, AWS
Designed multi-agent AI workflows using LangGraph with ReAct/ReWOO architectures, stateful pipelines, conditional routing, memory management, and human-in-the-loop checkpoints.
Tech: LangGraph, LangChain, OpenAI, Streamlit
Built a Python-based MCP server using FastMCP for standardized, secure tool access for LLM agents. Enables agents to query Databricks and Snowflake without exposing credentials.
Tech: MCP, FastMCP, Databricks, Snowflake, FastAPI, Docker
Full-stack AI mock interview platform with FastAPI backend, GPT-4 conversational engine, dynamic question generation, and structured performance scoring.
Tech: FastAPI, Streamlit, OpenAI GPT-4, Docker
Python-based FastMCP server providing standardized, secure tool access for LLM-driven AI agents
Agents query Databricks SQL warehouses and Snowflake databases without exposing credentials in prompts
Secure credential resolution, request validation, retry logic, structured logging, and observability
Rapid onboarding of new enterprise data systems with minimal configuration
GitHub Actions for automated testing and deployment of MCP server updates
Technologies: Python, MCP, FastMCP, Databricks, Snowflake, FastAPI, Docker, GitHub Actions
Wichita State University, Kansas, USA
GPA: 3.84 / 4.0
Graduated: May 2023
Birla Institute of Technology, India
GPA: 3.67 / 4.0
Graduated: May 2018
Amazon Web Services - Validates expertise in AI/ML services and generative AI on AWS
Microsoft Certified - Designing and implementing Azure AI solutions
365 Data Science - OpenAI, LangChain, Vector Databases, Fine-Tuning
365 Data Science - MCP for AI Systems - Secure agent-to-data connectivity
Built GenAI RAG platform enabling operations and finance teams with ~60% faster information retrieval across 50K+ documents. Deployed MCP-based connectors for secure agent-to-data access.
Delivered LLM-powered document analysis reducing manual review by ~40%. Ensemble forecasting models improved demand planning accuracy by 30-45%.
Built gradient boosting models for churn prediction, lead scoring, and revenue forecasting used by sales leadership for quarterly planning.
Built forecasting models (Prophet, ARIMA, LSTM) for supply chain and financial planning. Automated ML workflows with containerized pipelines on AWS.
Built Python automation frameworks achieving 99% data accuracy, ETL pipelines, and reporting dashboards in Power BI and Tableau.
MLflow for versioning, metrics logging, and model registry with comprehensive experiment management
Docker + Docker Compose for reproducible builds; ECS with blue-green deployment patterns and autoscaling policies
GitHub Actions for automated testing, building, and deployment; Amazon ECR for container image management
CloudWatch dashboards for real-time monitoring, alerting, and observability of AI services and APIs
IAM role-based authentication, API Gateway security controls, and MCP-based credential management for agents
RAGAS and DeepEval for LLM output evaluation; custom scoring rubrics and automated evaluation pipelines before production rollout
Established comprehensive MLOps practices supporting 15+ production models with consistent performance, automated monitoring, and rapid iteration cycles.
Optimized Databricks pipelines using Delta Lake, partitioning strategies, and query optimization. Built batch forecasting jobs with S3 artifact storage and scheduled weekly model refresh cycles.
Deep AWS expertise: S3 for artifact storage and dataset versioning with IAM access controls. Migrated on-premise analytics workloads to AWS EC2 and S3 with lifecycle management policies.
Worked cross-functionally with product, architecture, and business teams to operationalize AI across 10+ teams, translating complex requirements into scalable AI solutions
Drove successful adoption of GenAI tools across operations and finance teams at AUConnects, enabling 500+ users with self-service AI capabilities
Presented AI solutions and results to business leadership; built reporting dashboards in Power BI and Tableau for monthly performance reviews
Collaborated with engineering teams on containerized deployment patterns, CI/CD best practices, and MLOps workflows across multiple organizations
Delivered production AI systems iteratively with version-controlled deployment workflows, A/B testing, and continuous monitoring for reliability
I'm passionate about building production AI systems that solve real business problems. With 8+ years of experience in GenAI, RAG architectures, agentic workflows, and end-to-end ML pipelines, I bring both deep technical expertise and a track record of measurable impact.
Currently open to senior AI/ML Engineer roles focused on Generative AI, Agentic Systems, and Production ML — remote or relocation to Atlanta, GA.
📧 ramu3data@gmail.com
📞 316-372-6764
🔗 LinkedIn: linkedin.com/in/ramu-ganta-002a90141
💻 GitHub: github.com/RamuGanta
🌐 Website: https://ramuganta.com/
AI/ML Engineer | Generative AI & Agentic Systems