AI Product Features
From LLM prototypes to reliable production endpoints.
AI-ML / Backend EngineerOpen to projects and consultations
Building intelligentand Scalablesystems.
I help teams ship products with modern AI-ML and scalable production-grade backend architecture.
Core build tracks I typically deliver for product teams:
Backend Core
High-throughput APIs and resilient service architecture.
MLOps + Reliability
Model lifecycle and operational safety from day one.
About Me
AI engineering and backend execution across data, models, APIs, and production infrastructure.
What I bring
I work with founders and product teams as an AI engineer and backend engineer, turning ideas into reliable production systems with clean architecture and strong observability.
I focus on practical delivery, clear interfaces, and systems that teams can ship, debug, and extend without unnecessary complexity.
- AI and machine learning systems from prototype to deployment
- Scalable backend APIs with robust data contracts and integrations
- Monitoring, alerting, and cost-aware optimization for production workloads
Current Focus
Shipping LLM features, retrieval systems, and event-driven backend services for teams that need practical machine learning engineering and production reliability.
Open to freelance projects, consulting, and software engineering roles where strong execution matters.
- RAG quality tuning, grounding, and citation-aware responses
- Low-latency inference with caching and queue-based backend execution
- Production observability for model quality, drift, and API health
Tech Stack
Technologies I use to build AI features and resilient backend systems.
AI and ML
Model development, serving, and evaluation
PyTorchTensorFlowScikit-learnKerasOpenCVLangChainCrewAI
Backend and Data
Service APIs and data-intensive workflows
PythonFastAPIDjangoPostgreSQLMongoDBRedisMySQL
Infrastructure and Ops
Deployment, orchestration, and observability
DockerKubernetesAmazon Web ServicesGoogle Cloud PlatformMicrosoft Azure
Featured Work
Recent systems built for performance, reliability, and measurable impact.
Web Application
Research-AI
Developed a full stack Deep Research platform for generating citation-grounded long format research documents with multi-agent architecture.
Beats OpenAI, Gemini, Perplexity and other deep research agents on DeepResearch Bench benchmark test.
PythonFastAPIReact.jsLangChainFirebaseOAuth
Desktop Application
Computer Use Agent
Multimodal agent that can perform tasks on a computer based on natural language instructions using vision and tools.
Can run 100% locally or use a hybrid approach with local and cloud-based LLMs to perform long and complex tasks
PythonMultimodalLocal LLMsOpenCVLangChain
Internal Platform
Internal Multi-Agent Platform
Built and deployed a microservice-based multi-agent platform with real-time RAG pipeline, multiple data source integrations and sessioned chats.
E2E latency of <7s; custom analytics and monitoring dashboard; RBAC and SSO integration for the dashboard and agents
Comoto Holdings, USA
GCPLLMOpsPostgreSQLFastAPILangChainReact.js
Model Deployment
Health Device Information Classification
Trained and evaluated 120+ classification models on sensor/health signals; compared against baselines and iterated on features & validation.
Average accuracy of ~85% across multiple device types; processed 10,000+ records; deployed models with scheduled retraining pipeline
Samsung R&D Institute India - Bengaluru
PythonMulticlass ClassificationData ProcessingMLOps
Services
Freelance AI engineering, machine learning engineering, and backend support for production teams.
AI/ML Engineering
LLM features, RAG systems, prompt pipelines, evaluation frameworks, and production-ready AI workflows.
Backend Architecture
Scalable API design, domain modeling, auth patterns, service boundaries, and maintainable backend systems.
MLOps and Model Serving
Model versioning, deployment automation, inference optimization, and reliable serving for production ML systems.
Data Pipelines and Integrations
Batch and streaming pipelines, ETL orchestration, and backend integrations that keep production data moving.
Observability and Monitoring
Telemetry, alerts, dashboards, and SLO tracking across APIs, AI services, and ML workloads.
FAQ
Common questions before we build.
What kinds of clients do you usually work with?
Mostly SaaS teams, AI startups, and product companies that need production-grade backend and ML execution without expanding permanent headcount.
Can you work with our existing backend stack?
Yes. I usually integrate with existing services and improve architecture incrementally, unless a clean rebuild is clearly the better long-term option.
Do you also handle ML deployment and monitoring?
Yes. I cover model serving, deployment workflows, metrics, logging, and alerting so ML features stay reliable after release.
How quickly can we start?
Usually within 3 to 7 days depending on scope and timeline. Architecture audits can often begin within 48 hours.
Contact
Need help shipping an ML feature or scaling your backend?
Share your use case and current stack, and I'll send a focused technical plan within 24 hours.
Or email me at:
nabhpatodi1005@gmail.com
Schedule A Call