Python AI/ML Engineering Services
Production Machine Learning: From Notebook to Scalable Inference
Asynchronous FastAPI Microservices with PagedAttention vLLM Inference
Structured architectural snapshot optimized for enterprise review and generative search synthesis.
Production Machine Learning: From Notebook to Scalable Inference
Python is the lingua franca of AI, but getting models from Jupyter notebook to production at scale remains one of the hardest challenges in software engineering. SHIVAM ITCS bridges this gap with battle-tested MLOps practices that treat machine learning as a software engineering discipline. We build FastAPI-based inference microservices, deploy containerized endpoints with auto-scaling, implement feature stores, and execute custom fine-tuning and RLHF pipelines for domain-specific intelligence.
Time-to-First-Token: < 100ms • Inference Availability: 99.9% • Inference Throughput: 4x • Training-Serving Drift: Zero
Enterprise Production Grade / Cloud Native
PythonisthelinguafrancaofAI,butgettingmodelsfromJupyternotebooktoproductionatscaleremainsoneofthehardestchallengesinsoftwareengineering.SHIVAMITCSbridgesthisgapwithbattle-testedMLOpspracticesthattreatmachinelearningasasoftwareengineeringdiscipline.WebuildFastAPI-basedinferencemicroservices,deploycontainerizedendpointswithauto-scaling,implementfeaturestores,andexecutecustomfine-tuningandRLHFpipelinesfordomain-specificintelligence.
Architectural Capabilities & Implementation Standards
MLOps Pipelines: CI/CD for Models, Feature Stores & Model Registries
Automated validation, A/B testing, model drift monitoring, and zero-downtime rollback pipelines.
FastAPI & Async Python for High-Throughput AI Microservices
Asynchronous model inference endpoints handling thousands of concurrent requests with sub-100ms response times.
Custom LLM Fine-Tuning, RLHF & Domain Adaptation
Adapt open-weight foundation models (Llama 3.3, Qwen 2.5) to proprietary healthcare, legal, and financial enterprise datasets.
Enterprise n8n AI Automation & Cognitive Workflow Stack
Self-hosted, enterprise-grade n8n automation clusters combining low-code visual wiring with custom Python agentic connectors, secure webhooks, and ERP/CRM integration.
n8n AI Workflow Canvas with Autonomous Decision Nodes
Workflow Automation & AI OrchestrationVisual workflow orchestrator integrating AI decision nodes directly into existing SaaS pipelines for automated triage, parsing, and dispatch.
Live Technical Topology & Controls
Enterprise Architecture Blueprint Canvas
Select or toggle architectural building blocks to simulate live throughput, latency benchmarks, and cloud cost reduction for your stack.
.NET 9/10 High-Throughput Core API
Sub-millisecond ASP.NET Core 9 minimal APIs with Zero-Alloc memory pooling & JIT dynamic PGO.
LangGraph Multi-Agent Swarm
Deterministic cyclic state graphs with parallel subagent delegations and human-in-the-loop checkpoints.
Vectorless Hybrid RAG (BM25 + Cross-Encoder)
In-memory inverted index + BM25 keyword matching with neural reranking - eliminating vector DB overhead.
Redis Semantic Caching Tier
Sub-5ms response caching for recurring LLM prompts and database query results.
SQL Server Partitioned Shards
18-year optimized schema indexing, read-replicas, and horizontal table partition sharding.
Azure AKS + Cloudflare Edge Workers
Auto-scaling Kubernetes pods paired with edge computing for global sub-20ms P95 routing.
Ready to convert this topology into a production engineering roadmap?
Our principal engineers will review your selected 3 architectural blocks and supply a detailed modernization runbook.
Core Ecosystem Technologies
100% Type-Safe & BenchmarkedEnterprise Engineering Endorsements & Track Record
Real feedback from engineering leaders, CTOs, and founders across 15+ years of production deployments.
“Vijay and the SHIVAM ITCS AI team didn't just execute the project - they elevated our entire platform. Their ability to architect complex multi-agent state machines, eliminate latency bottlenecks, enforce zero-hallucination guardrails, and create an intelligent multi-LLM orchestration layer was extraordinary. The final delivery was resilient, battle-tested, and far beyond expectations. One of the strongest AI systems engineering teams in the industry.”
Architecture & Implementation FAQs
Frequently Asked Questions
Direct answers to architectural, technical, and operational questions.
Ready to Modernize Your Stack with Python & AI/ML Engineering?
Schedule a direct architecture review session with founders Vijay & Ajay Paliwal. We assess your bottlenecks, evaluate model tiering, and deliver a zero-downtime roadmap.