You will get Production-ready Full Enterprise Agent System
Project details
You will get a production-ready, autonomous Multi-Agent AI System built for enterprise-grade scalability, determinism, and sub-second execution latency.
As a Senior Engineer specializing in MLOps, LLMOps, and Agentic AI architectures across GCP, AWS, and Azure, I build systems that go far beyond basic wrappers. I engineer self-healing, state-driven agent topologies (Supervisor-Worker DAGs, Model Context Protocol integration) capable of autonomous decision-making, dynamic tool calling, and high-throughput vector retrieval.
What sets this service apart:
• Enterprise Orchestration: Built on LangChain/LangGraph, Gemini, and custom tools with strict state management (Redis/DynamoDB) and hybrid search (Elasticsearch).
• Production Guardrails: Real-time PII masking, prompt-injection defense, and token optimization to manage unit economics.
• End-to-End LLMOps: OpenTelemetry & Langfuse tracing, Docker containerization, Kubernetes manifests, and clear CI/CD deployment docs.
Whether you need agentic API automation, complex RAG, or multi-agent workflows, you get robust, battle-tested code built for production reliability.
As a Senior Engineer specializing in MLOps, LLMOps, and Agentic AI architectures across GCP, AWS, and Azure, I build systems that go far beyond basic wrappers. I engineer self-healing, state-driven agent topologies (Supervisor-Worker DAGs, Model Context Protocol integration) capable of autonomous decision-making, dynamic tool calling, and high-throughput vector retrieval.
What sets this service apart:
• Enterprise Orchestration: Built on LangChain/LangGraph, Gemini, and custom tools with strict state management (Redis/DynamoDB) and hybrid search (Elasticsearch).
• Production Guardrails: Real-time PII masking, prompt-injection defense, and token optimization to manage unit economics.
• End-to-End LLMOps: OpenTelemetry & Langfuse tracing, Docker containerization, Kubernetes manifests, and clear CI/CD deployment docs.
Whether you need agentic API automation, complex RAG, or multi-agent workflows, you get robust, battle-tested code built for production reliability.
AI Algorithms
Autoencoder, Feedforward Neural Network, Generative Adversarial Network, Large Language Model, Long Short-Term Memory Network, Multimodal Large Language Model, Recurrent Neural Network, Regression Analysis, StyleGAN, Transformer ModelAI Applications
AI Chatbot, AI Content Creation, AI Text-to-Image, AI Text-to-Speech, AIOps, Anomaly Detection, Image Analysis, Natural Language Generation, Natural Language Understanding, Sentiment Analysis, Synthetic Data Generation, Time Series AnalysisAI Development Language
PythonAI Tools
Azure OpenAI, GitHub Copilot, Gradio, Hugging Face, Microsoft 365 Copilot, PyTorch, Streamlit, TensorFlow, Word2vecAI Models
BERT, BLOOM, ChatGPT, DALL-E, GPT-3, GPT-4, LaMDA, LLaMA, Midjourney AI, OpenAI Codex, Stable Diffusion, WhisperWhat's included
| Service Tiers |
Starter
$1,500
|
Standard
$3,500
|
Advanced
$8,000
|
|---|---|---|---|
| Delivery Time | 30 days | 50 days | 70 days |
Number of Revisions | 2 | 3 | 5 |
AI Model Integration | |||
Batch Normalization | - | - | - |
Database Integration | - | ||
Detailed Code Comments | |||
Image Upscaling | - | - | - |
MLOps | - | - | |
Model Deployment | - | ||
Model Documentation | |||
Model Monitoring | - | - | |
Model Testing & Optimization | - | ||
Model Tuning | - | - | |
Natural Language Processing | |||
NLP Tokenization | |||
Pre-Training | - | - | - |
Prompt Engineering | |||
Setup File | |||
Source Code |
Frequently asked questions
About Aniruddha
Staff AI Engineer | Production-Grade GenAI Backends & Multi-Agent Mesh
Bengaluru, India - 12:44 am local time
I am a Lead AI Engineer, Multi-Patent Holder, and published Book Author with over 10 years of experience designing, operationalizing, and securing complex, high-throughput AI infrastructure. My background bridges the gap between deep financial-grade software reliability (American Express, Wells Fargo) and the absolute bleeding edge of autonomous agentic networks.
As a recognized thought leader in the space, my work is backed by published literature and patented IP, including:
Published Book: Continuous Machine Learning with Kubeflow.
Published Book: Agentic Commerce.
Patent One: Smart Payment Routing With Reward Incentives For Transaction Failures.
Patent Two: Dynamic Resource Allocation and Tracking Using a Multi-Factor Allocation Engine.
I specialize in merging classical Machine Learning with modern Large Language Models and multi-agent systems to solve high-stakes challenges in the financial and retail sectors. Whether you are building an autonomous, protocol-compliant e-commerce backend or an AI-driven transactional framework, I engineer solutions governed by strict software reliability, compliance, and enterprise standards.
🧰 Core Skills & Technical Expertise
Agentic Commerce & E-Commerce AI: Universal Commerce Protocol (UCP), profile discovery, capability negotiation, services-capabilities-extensions modeling, identity linking, Merchant of Record (MoR) architectures, trust triangles, dynamic checkout/cart flows, and personalized product discovery engines.
Fintech AI & Secure Payments: AP2-compatible agent payments, automated payment routing engines, financial risk modeling, predictive transaction analytics, asset forecasting, and compliance-aligned workflows.
Generative AI & Agentic Frameworks: LangGraph, Model Context Protocol (MCP), A2A Protocol, OpenAI Agents, Google Agentic Kit, Custom Copilot Studio Connectors, Advanced Reasoning (Plan-then-execute, Tree-of-Thought, Graph-of-Thoughts).
LLMOps & Model Fine-Tuning: Local LLM Fine-Tuning (Llama, Hugging Face, DeepSpeed), LoRA/QLoRA via UnSloth, quantized inference hosting (vLLM, TGI), prompt engineering, and model evaluation (Ragas, Argilla, LangKit).
Classical Machine Learning & NLP: XGBoost, Ensemble Models, Deep Learning (Transformers, LSTM, CNNs), Time-Series Forecasting, Recommender Systems, Statistical Modeling, Predictive Analytics, spaCy, NLTK, PyTorch, TensorFlow.
Enterprise AI Security & Governance: Model Risk Management (MRMG), prompt injection defense, jailbreak mitigation, PII tokenization, OKTA SSO, API Gateway authorization, OAuth 2.0, mTLS, zero-trust agent meshes.
Data Architecture & MLOps Infrastructure: Production-scale search-first backends (FastAPI, Flask), Elasticsearch, OpenSearch, Neo4j Knowledge Graphs, Vector DBs (pgvector, Pinecone, FAISS, ChromaDB), Apache Spark, BigQuery, Kubeflow, MLflow, Kubernetes (GKE), Docker, Redis, CI/CD (GitHub Actions, ArgoCD), Grafana, LangSmith, Langfuse.
Cloud Environments: Google Cloud Platform (GCP), Amazon Web Services (AWS), Microsoft Azure.
📈 Proven, Data-Driven Production Impact
Agentic Commerce (Retail): Designed and executed an end-to-end agentic e-commerce platform for retail clients using a Reason-Act architecture with FastAPI and Elasticsearch, driving conversational product discovery and dynamic multi-agent capability negotiations.
Fraud Risk (Decentralized Crypto): Built classical ML and graph-based tracking pipelines to analyze decentralized crypto transactions, identifying wallet clustering behavior and anomalous patterns to detect fraud risks in real time.
Credit Risk (Decentralized Loans/Ethereum): Developed time-series and predictive regression models deployed on the Ethereum network to evaluate lending health, collateral volatility, and credit default risk for decentralized smart-contract loan protocols.
Root Cause Analysis (SRE Fintech): Deployed a multi-agent autonomous SRE diagnostic system using LangGraph and MCP server endpoints, parsing telemetry logs with 92% routing accuracy to automate triage for 115+ incident categories and drop MTTR by 20%.
Supply Chain Agentic Mesh: Engineered a multi-cloud automated agent mesh handling cross-protocol backend systems, establishing secure tool gateway proxies to dynamically orchestrate stock, demand forecasting, and vendor dispatch.
Kubeflow Platform (Retail): Architected custom Kubeflow MLOps pipelines on GKE with GitOps, productionizing 6 major AI models for real-time serving, slicing refresh cycles from 14 days to 4 days, and maintaining a 90%+ prediction accuracy.
Enterprise Scale & Governance: Authored 5 internal platform RFCs adopted across global engineering teams to standardize RAG evaluation and agent orchestration, influencing over 250+ engineers while clearing 3 consecutive regulatory audit cycles.
Steps for completing your project
After purchasing the project, send requirements so Aniruddha can start the project.
Delivery time starts when Aniruddha receives requirements from you.
Aniruddha works on your project following the steps below.
Revisions may occur after the delivery date.
Architecture Alignment & Tool/API Discovery
Review business ROI goals, domain compliance , and system constraints. Audit OpenAPI specs, custom tools, and data schemas to map out the agent execution graph and state persistence topology.
Core Multi-Agent Orchestration & State Memory
Implement the primary supervisor/worker DAG using LangChain/Gemini etc. Configure state memory (Redis/DynamoDB etc), vector indexing (Elasticsearch/Qdrant etc), and custom tool calling with strict input/output validation.