RamKumar Manoharan

5.0

(7 reviews)

US$20.00

For every 15 mins

Sessions/Jobs

ABOUT ME

Agentic A.I Architect

Agentic AI Architect | Designing Autonomous AI Systems, Multi-Agent Workflows & Enterprise GenAI Solutions

I specialize in architecting and building next-generation Agentic AI systems that combine Large Language Models (LLMs), autonomous agents, retrieval systems, and workflow orchestration to solve real-world business problems at scale. My work focuses on creating intelligent AI ecosystems capable of reasoning, planning, tool usage, memory management, and multi-step decision making across enterprise environments.

With 16+ years of experience in AI, Machine Learning, Data Engineering, and Generative AI, I have designed and implemented production-grade AI solutions spanning AI Agents, RAG systems, Knowledge Graph integration, AI copilots, intelligent automation, and multi-agent orchestration frameworks.

Key Areas of Expertise
Agentic AI System Design & Architecture
Multi-Agent Collaboration Frameworks
Retrieval-Augmented Generation (RAG)
Knowledge Graph + LLM Integration
AI Copilots & Autonomous Workflows
Prompt Engineering & Reasoning Systems
AI Evaluation & Hallucination Reduction
LLM Fine-Tuning & Optimization
AI Workflow Automation
Enterprise AI Solution Engineering
Conversational AI & AI Assistants
AI Governance, Safety & Reliability
Technologies & Frameworks
Python, FastAPI, Streamlit
LangChain, LangGraph, CrewAI, Semantic Kernel
OpenAI GPT, Claude, Gemini, Llama, Mistral
Amazon Bedrock, Azure AI Foundry, AWS SageMaker
ChromaDB, Pinecone, FAISS, Weaviate
Neo4j Knowledge Graphs
Hugging Face Transformers
Docker, Kubernetes, APIs, MCP-based Architectures
Vector Databases & Embedding Pipelines
AI Monitoring & Evaluation Frameworks
Areas I Mentor & Consult On
Building AI Agents from Scratch
Enterprise Agentic AI Architecture
RAG Pipeline Development
Multi-Agent System Design
LangGraph & CrewAI Implementations
Knowledge Graph for LLMs
AI Product Architecture
Generative AI Project Guidance
AI Startup Technical Strategy
Production Deployment & Scaling of AI Systems

I actively mentor developers, startups, engineers, and enterprises in transforming AI concepts into scalable intelligent systems with a strong focus on practical implementation, architecture clarity, and industry-ready solutions.

English

Chennai (+05:30)

Joined October 2019

EXPERTISE

7 years experience

7 years experience

5 years experience

3 years experience

3 years experience

2 years experience

Deep Learning Cnn RnnGan

4 years experience

REVIEWS FROM CLIENTS

5.0

(7 reviews)

Santiago Buch

June 2021

Request completed on time and with high quality

edithc2010

December 2019

Ram explained concepts really well and discussed the reasons why he chose to go with a certain process. Great session!

Sara

December 2019

Ram is very knowledgeable and helpful as he is one of the best mentors

Sara

November 2019

great

Simileoluwa Ajayi

November 2019

Ram was kind, polite and understanding throughout the course of the project. He completed my project more than five days before the actual deadline I set and did a really good job as well. I would definitely recommend him to a friend or anyone who needs help with any machine learning project.

SOCIAL PRESENCE

GitHub

Predict-Bike-Sharing-Demand-with-AutoGluon

Predict Bike Sharing Demand with AutoGluon Project submission

Jupyter Notebook

Build-AI-Enriched-Corporate-Training-Catalog

HTML

Community Posts

The Rise and Productization of Large Language Models

Explore the exciting advancements in large language models (LLMs) in our latest blog post. We delve into recent trends, the productization process, and the increasing adoption of these models across industries. Learn how models like OpenAI's ChatGPT and Google's Bard are revolutionizing processes and creating new possibilities.

EMPLOYMENTS

Senior AI Engineer

TensorLearners

2021-05-01-2023-11-01

Senior Manager at TensorLearners, a consulting startup that helps organizations adopt and effectively use advanced machine learning an...

Senior Manager at TensorLearners, a consulting startup that helps organizations adopt and effectively use advanced machine learning and artificial intelligence technologies

Product Management and Solution Architecture - Generative A.I

Graph DB Integration with Gen A.I product

My role and responsibilities services include consulting clients on MLOps adoption, advanced machine learning approaches, AI technology architecture design, intelligent response system development, and corporate training. Worked on integrating LLM models for workflow using packages such as langchain, LLamaIndex

Python

SQL

PyTorch

Python

SQL

PyTorch

OpenAI

Generative AI

LLM

GPT-4

Senior Data Scientist and Machine Learning Engineer

Anheuser-Busch InBev

2019-04-01-2021-04-01

Senior Data Scientist , with demonstrated history of handling and building end to end Machine Learning Models for Business Problems acros...

Senior Data Scientist , with demonstrated history of handling and building end to end Machine Learning Models for Business Problems across Industry. Currently part of Anheuser-Busch InBev Data Science Team, working on strategic initiatives driven by data.

Python

MySQL

Azure

Python

MySQL

Azure

Machine Learning

Data Science

Data Scientist

Infosys

2011-05-01-2019-05-01

Data Scientist Managed end to end Data Science , Machine Learning Projects

Python

Machine Learning

Computer Vision

Python

Machine Learning

Computer Vision

Deep Learning

TensorFlow

PROJECTS

Clustering Customer Complaint Data for a Manufacturing Company

2019

Idea is to Analyze the unstructured data and identify the critical clauses / Phrases which point the complaint issues, where companies ca...

Idea is to Analyze the unstructured data and identify the critical clauses / Phrases which point the complaint issues, where companies can pinpoint and rectify the issues or use Preventive Mechanism. Techniques Used: • Vectorization method used: TF – IDF (term frequency, inverse document frequency) 3 • Clustering Method – TF IDF Clustering. We cannot directly visualize TD IDF since the data is in sparse matrix and require dimension reduction, as TF IDF clustering is not a common clustering method. • Conversion of the TF-IDF score into format which has (X, Y) coordinates. Truncated SVD - which is linear dimension reduction by Singular Value Decomposition - Sparse matrix into A dense matrix. - Still we cannot visualize since it has higher dimension with random Probability of occurrence. In simple words, the data is Stochastic. - We need to use t-SNE method. (t – distributed Stochastic Network Embedding) - From this analyze, we can identify some of phrases which can have impact - Used K Mean Clustering as secondary method.

Python

Machine Learning

Clustering

Python

Machine Learning

Clustering

NLP

Using Machine Learning to Optimize Customer Remediation Process

US based Finance Company

2019

Large Multinational Financial Company wants to optimize its customer remediation process by incorporating Machine learning. The company n...

Large Multinational Financial Company wants to optimize its customer remediation process by incorporating Machine learning. The company needs to identify the impacted customers on a regular basis for a given issue and to predict the appropriate remediation amount to be paid. As part of the project, I built a Machine learning model using ensemble techniques on random forest and gradient descent methods to identify the impacted population and to predict remediation amount range. This model on subsequent training and tuning yielded 91 % accuracy in predicting the amount range and was fully incorporated in the client process.

Python

Azure

Machine Learning

Python

Azure

Machine Learning