AI Architecture & System Design for Production
I help teams design, review, and de-risk AI systems—from GenAI and RAG to multi-agent workflows and data platforms—with a focus on production readiness, cost efficiency, and measurable outcomes.
Architecture Reviews
RAG/agent designs, data flows, security, and scaling — with clear recommendations.
System Design
Reference architecture, tradeoffs, APIs, and operational plan for production rollout.
PoC → Production
Turn prototypes into reliable systems with evaluation, observability, and guardrails.
Who this is for
Teams building AI features that must work in the real world: with messy data, real users, compliance, budgets, and uptime requirements.
Engineering teams shipping GenAI, RAG, Multi-Agent, or MCP-powered workflows
Founders validating whether AI or Data Science is worth building
Leads who want an architecture or data science second opinion before committing
Managers asked to “add AI, Multi-Agent, or Data Science” without a system design plan
What I help with
Practical guidance across AI product, architecture, and delivery — from first feasibility checks to production hardening.
Feasibility & ROI
- →Use-case clarity and success metrics
- →Model vs rules vs search tradeoffs
- →Cost, latency, and risk estimation
- →Data readiness and data science opportunity analysis
RAG & Knowledge Systems
- →Indexing strategy, chunking, retrieval, reranking
- →Grounding, citations, and hallucination controls
- →Evaluation: relevance, faithfulness, and drift
- →Data pipelines, feature engineering, and data quality
Multi-Agent & Agentic Workflows
- →Tooling boundaries and failure modes
- →Human-in-the-loop checkpoints
- →State, retries, and idempotency
- →Multi-agent orchestration, communication, and collaboration
- →MCP (Model Context Protocol) integration
Production Readiness
- →Observability: traces, prompts, cost dashboards
- →Security & privacy: PII, secrets, access controls
- →Reliability: SLAs, fallbacks, and safe degradation
- →LLMOps, Responsible AI, and continuous evaluation
Architectures & system design
I design systems end-to-end: user workflow → APIs → data → compute → evaluation → monitoring. Below are common reference architectures I help teams implement.
RAG for enterprise knowledge
When to use: When answers must be grounded in internal documents/data.
What’s inside: Ingestion, chunking, embeddings, vector store, reranking, citations, eval.
Agent + tools (workflow automation)
When to use: When tasks require calling APIs, updating systems, and multi-step reasoning.
What’s inside: Tool design, permissions, state, retries, human approvals, audit logs.
AI copilots inside products
When to use: When users need guided actions: search, summarize, draft, and decision support.
What’s inside: UX patterns, guardrails, prompt contracts, telemetry, A/B + evaluation.
Data platform for ML/AI & Data Science
When to use: When data reliability or advanced analytics is the bottleneck for any model, GenAI, or business system.
What’s inside: Pipelines, data contracts, feature/data stores, quality checks, lineage, analytics, and data science workflows.
How I work
Clear, system-first thinking. I aim to reduce ambiguity, make tradeoffs explicit, and produce artifacts your team can execute.
1) Discovery
Understand goals, constraints, current stack, data, and users.
2) System design
Define architecture, interfaces, components, and failure modes.
3) Evaluation plan
Define test sets, metrics, review loops, and launch criteria.
4) Delivery support
Help your team implement, iterate, and harden for production.
Deliverables you’ll receive
You’ll leave with concrete outputs your team can implement — not just a call recording.
Architecture pack
- →High-level diagram + component responsibilities
- →Key tradeoffs and recommended decisions (ADRs)
- →Failure modes and mitigations
System design details
- →Data flows, APIs, and sequence of operations
- →Security & privacy considerations
- →Cost/performance notes and scaling plan
Evaluation plan
- →Metrics and acceptance criteria
- →Golden set / test set strategy
- →Monitoring + feedback loop recommendations
Execution roadmap
- →Prioritized backlog for MVP/PoC
- →Risks, unknowns, and validation steps
- →Phased rollout strategy
Engagement options
1:1 architecture review
A focused session to review your AI system design and identify risks, tradeoffs, and next steps.
- • 60 minutes • video call
- • You share context + diagrams (if available)
- • You receive written recommendations
System design sprint
Short engagement to produce a production-ready architecture, evaluation plan, and rollout approach.
- • 1–2 weeks
- • Architecture pack + roadmap
- • Stakeholder-friendly docs
Build with your team
Hands-on support to implement, evaluate, and ship — with guardrails, monitoring, and reliability.
- • Part-time / project-based
- • Pairing + reviews + PR guidance
- • Confidential, practical, fast
Background
I’ve worked on data science and AI systems across domains including supply chain, healthcare, and enterprise analytics. I’ve built and reviewed large-scale pipelines, GenAI applications, agent workflows, and optimization models (including MILP) on cloud platforms.
I contribute as both an individual contributor and a technical lead. Client and employer details are anonymized due to confidentiality.
Architectures, notes, and breakdowns
I publish system-thinking content and practical AI engineering notes. More written breakdowns will be added here over time.
YouTube: architecture + AI engineering
Deep dives, design tradeoffs, and practical build guidance.
Request a topic
Tell me what architecture/system design you want covered.
Recent videos
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MCP under 2 mins . #ai
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Vector Search || Semantic Search || RAG || AI basics
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motherboard selecting tips || Telugu || chipset || desktop selection
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Client testimonial
"Krishna''s guidance helped us save 30% on infrastructure costs while scaling our AI system reliably. His cost-effective approach and practical architecture recommendations were exactly what we needed."
FAQ
Do you work with startups and enterprises?
Yes. The approach is the same: clarify outcomes, design the system, and ensure it’s shippable and measurable.
Can you review an existing architecture?
Yes. I’ll review your diagrams/code notes, identify risks, and propose concrete fixes and tradeoffs.
Do you build end-to-end?
I can support implementation with your team (pairing, reviews, guidance). Full delivery depends on scope and timelines.
What do you need from us to start?
A short problem statement, current stack, sample data/doc sources (if RAG), and any constraints (security, latency, budget).
Get in touch
If you’re unsure whether something makes sense, start with a conversation. Share your context and I’ll reply with a suggested next step.