Shrikant Kashi
Raipur, India
Shrikant Kashi
AI Systems Architect & Strategic Advisor
Category : Artificial intelligence (AI)
Bridging the Gap Between "Impressive Demos" and Production-Grade Reliability.
Most enterprise AI initiatives fail because they treat LLMs and Agents as traditional software. I help CTOs and Engineering Leaders move away from manual "vibe-checking" and brittle prototypes toward systematic, governed, and data-driven architectures.
As a Principal AI Systems Architect (MBA), I provide the independent technical judgment required to deploy autonomous agents that are safe, deterministic, and ROI-positive from Day 1.
Core Service Pillars:
1. Enterprise Agentic Architecture (The "Context Mesh") I design Zero-Trust ecosystems that allow LLMs to interact securely with legacy enterprise silos (SQL, SAP, CRM, ERP).
Model Context Protocol (MCP): Implementing standardized hubs for tool-calling and data retrieval.
Architecture Blueprints: Providing the "Enterprise Agentic Suite" framework to ensure modularity and prevent vendor lock-in.
2. AI Governance & Operational Control I build the "Brain" of the system to prevent "Agentic Sprawl" and runaway costs.
Deterministic Guardrails: Hard-coding execution boundaries to prevent unauthorized actions or infinite loops.
Cost & Latency Optimization: Model routing logic to balance performance (e.g., GPT-4o) with efficiency (e.g., Haiku/Flash).
Human-in-the-Loop (HITL): Native integration for high-stakes approvals ($$$ transactions).
3. Reliability & Systematic Evaluation I deploy the "Conscience" of your AI stack to ensure long-term stability.
Automated Regression Testing: Measuring intent drift and hallucination rates.
Observability Pipelines: Real-time monitoring of agent health and groundedness.
Security & Compliance: PII-scrubbing layers and audit-ready SIEM logging.
The Strategic Advantage:
My background combines deep technical implementation (Python 3.12, LangGraph, Docker) with an MBA-level focus on business strategy. I don’t just ship code; I ensure your AI roadmap aligns with your P&L, reduces technical debt, and meets the strict audit requirements of the 2026 regulatory landscape.
How I Work:
I operate as a "Player-Coach." I architect the core infrastructure (The Mesh, The Governance Logic, The Eval Harness) and then transition implementation ownership to your internal team.
Ideal for: Companies scaling beyond the prototype phase who need "Day 1" reliability in Banking, Healthcare, or Legal-Tech.
Most enterprise AI initiatives fail because they treat LLMs and Agents as traditional software. I help CTOs and Engineering Leaders move away from manual "vibe-checking" and brittle prototypes toward systematic, governed, and data-driven architectures.
As a Principal AI Systems Architect (MBA), I provide the independent technical judgment required to deploy autonomous agents that are safe, deterministic, and ROI-positive from Day 1.
Core Service Pillars:
1. Enterprise Agentic Architecture (The "Context Mesh") I design Zero-Trust ecosystems that allow LLMs to interact securely with legacy enterprise silos (SQL, SAP, CRM, ERP).
Model Context Protocol (MCP): Implementing standardized hubs for tool-calling and data retrieval.
Architecture Blueprints: Providing the "Enterprise Agentic Suite" framework to ensure modularity and prevent vendor lock-in.
2. AI Governance & Operational Control I build the "Brain" of the system to prevent "Agentic Sprawl" and runaway costs.
Deterministic Guardrails: Hard-coding execution boundaries to prevent unauthorized actions or infinite loops.
Cost & Latency Optimization: Model routing logic to balance performance (e.g., GPT-4o) with efficiency (e.g., Haiku/Flash).
Human-in-the-Loop (HITL): Native integration for high-stakes approvals ($$$ transactions).
3. Reliability & Systematic Evaluation I deploy the "Conscience" of your AI stack to ensure long-term stability.
Automated Regression Testing: Measuring intent drift and hallucination rates.
Observability Pipelines: Real-time monitoring of agent health and groundedness.
Security & Compliance: PII-scrubbing layers and audit-ready SIEM logging.
The Strategic Advantage:
My background combines deep technical implementation (Python 3.12, LangGraph, Docker) with an MBA-level focus on business strategy. I don’t just ship code; I ensure your AI roadmap aligns with your P&L, reduces technical debt, and meets the strict audit requirements of the 2026 regulatory landscape.
How I Work:
I operate as a "Player-Coach." I architect the core infrastructure (The Mesh, The Governance Logic, The Eval Harness) and then transition implementation ownership to your internal team.
Ideal for: Companies scaling beyond the prototype phase who need "Day 1" reliability in Banking, Healthcare, or Legal-Tech.
Portfolio
Working hours
- Monday:08h00 To 18h00
- Tuesday:08h00 To 18h00
- Wednesday:08h00 To 18h00
- Thursday:08h00 To 18h00
- Friday:08h00 To 18h00
- Saturday:Not available
- Sunday:Not available
![Principal AI Systems Architect (Enterprise Agentic Workflows), Strategic AI Advisor & ROI Consultant (MBA-Backed), AI Reliability & Governance:]()
Principal AI Systems Architect (Enterprise Agentic Workflows), Strategic AI Advisor & ROI Consultant (MBA-Backed), AI Reliability & Governance:250 $ - Per hour1. Principal AI Systems Architect Title: Principal AI Systems Architect (Enterprise Agentic Workflows) Description: I bridge the gap between experimental LLM prototypes and production-grade ...
Leading the transition from experimental LLMs to production-ready Agentic Architectures. I advise enterprise leadership on the deployment of autonomous systems that are governed, reliable, and ROI-positive.
Framework Design: Developed the 'Enterprise Agentic Suite,' a zero-trust architecture for secure multi-agent orchestration.
Reliability Engineering: Built automated evaluation platforms that eliminate hallucinations and ensure semantic integrity in high-stakes environments.
Strategic Integration: Bridging the gap between legacy ERP/CRM systems and frontier AI models using the Model Context Protocol (MCP).
Framework Design: Developed the 'Enterprise Agentic Suite,' a zero-trust architecture for secure multi-agent orchestration.
Reliability Engineering: Built automated evaluation platforms that eliminate hallucinations and ensure semantic integrity in high-stakes environments.
Strategic Integration: Bridging the gap between legacy ERP/CRM systems and frontier AI models using the Model Context Protocol (MCP).
Designing and building production-grade LLM and Agentic AI systems with a focus on reliability, evaluation, and enterprise readiness.
• Architected end-to-end Retrieval-Augmented Generation (RAG) systems with hybrid retrieval, reranking, and grounding strategies for high-precision outputs.
• Built multi-agent systems for orchestration, planning, tool use, and task decomposition, including failure handling and recovery flows.
• Developed an LLM Evaluation & Reliability Platform to measure hallucinations, faithfulness, latency, cost, and regression across model versions.
• Implemented LLMOps practices including prompt/version control, structured logging, observability, and automated evaluation pipelines.
• Designed guardrails, validation layers, and fallback strategies to improve system robustness under real-world failure modes.
• Focused on scalable, maintainable architectures aligned with enterprise deployment and long-term system evolution.
Tech focus: LLM systems architecture, agent orchestration, evaluation frameworks, and production reliability.
• Deployed and operated LLM systems on cloud infrastructure (AWS), focusing on scalability, cost control, and production observability.
• Architected end-to-end Retrieval-Augmented Generation (RAG) systems with hybrid retrieval, reranking, and grounding strategies for high-precision outputs.
• Built multi-agent systems for orchestration, planning, tool use, and task decomposition, including failure handling and recovery flows.
• Developed an LLM Evaluation & Reliability Platform to measure hallucinations, faithfulness, latency, cost, and regression across model versions.
• Implemented LLMOps practices including prompt/version control, structured logging, observability, and automated evaluation pipelines.
• Designed guardrails, validation layers, and fallback strategies to improve system robustness under real-world failure modes.
• Focused on scalable, maintainable architectures aligned with enterprise deployment and long-term system evolution.
Tech focus: LLM systems architecture, agent orchestration, evaluation frameworks, and production reliability.
• Deployed and operated LLM systems on cloud infrastructure (AWS), focusing on scalability, cost control, and production observability.
• Designed and implemented production-grade RAG pipelines with structured chunking, embeddings, and retrieval evaluation
• Built tool-using and multi-agent workflows with explicit orchestration, memory, and failure handling
• Developed API-first AI services with reliability, cost control, and observability in mind
• Focused on real-world constraints: latency, hallucination mitigation, and deployment readiness
• Built tool-using and multi-agent workflows with explicit orchestration, memory, and failure handling
• Developed API-first AI services with reliability, cost control, and observability in mind
• Focused on real-world constraints: latency, hallucination mitigation, and deployment readiness
Synthesized my technical background with an MBA in Finance to lead digital transformation initiatives. Focused on the intersection of technical infrastructure and fiscal responsibility.
Operational ROI: Managed technical roadmaps that prioritized efficiency and scalability, directly contributing to bottom-line growth.
Cross-Functional Leadership: Led teams of engineers and analysts to deploy software solutions that reduced manual operational dependency by over 30%.
Financial Governance: Oversaw technology budgets and vendor selection, ensuring long-term fiscal sustainability for complex system deployments.
Operational ROI: Managed technical roadmaps that prioritized efficiency and scalability, directly contributing to bottom-line growth.
Cross-Functional Leadership: Led teams of engineers and analysts to deploy software solutions that reduced manual operational dependency by over 30%.
Financial Governance: Oversaw technology budgets and vendor selection, ensuring long-term fiscal sustainability for complex system deployments.
Developed the core engineering discipline required to architect complex systems. Focused on backend stability, database optimization (SQL/NoSQL), and building the 'plumbing' that modern AI systems now sit upon. This hands-on foundation ensures that my current architectural advice is grounded in real-world technical feasibility.
Advanced specialization in the mechanics of frontier models. I focus on optimizing LLM performance through:
Advanced RAG: Implementing hybrid search and reranking for high-precision context retrieval.
Efficiency: Utilizing QLoRA for memory-efficient fine-tuning when domain-specific performance is required.
Context Management: Mastering long-context window handling to minimize 'lost-in-the-middle' phenomena in enterprise data.
Advanced RAG: Implementing hybrid search and reranking for high-precision context retrieval.
Efficiency: Utilizing QLoRA for memory-efficient fine-tuning when domain-specific performance is required.
Context Management: Mastering long-context window handling to minimize 'lost-in-the-middle' phenomena in enterprise data.
Specialized expertise in the 'Action Layer' of modern AI. I design autonomous and semi-autonomous systems using:
Agentic Orchestration: Building multi-agent systems that can plan, self-correct, and execute complex tool-use.
MCP (Model Context Protocol): Implementing standardized data connectors to bridge LLMs with secure internal databases and legacy SaaS tools.
Human-in-the-Loop (HITL): Designing escalation protocols for high-stakes decision-making environments.
Agentic Orchestration: Building multi-agent systems that can plan, self-correct, and execute complex tool-use.
MCP (Model Context Protocol): Implementing standardized data connectors to bridge LLMs with secure internal databases and legacy SaaS tools.
Human-in-the-Loop (HITL): Designing escalation protocols for high-stakes decision-making environments.
Bridging the gap between Balance Sheets and Neural Networks. With over 12 years of professional experience since my MBA, I specialize in the fiscal governance of AI systems. My financial background allows me to move beyond technical implementation to provide strategic oversight on:
AI ROI Modeling: Designing 12-month roadmaps that prioritize high-impact use cases over 'shiny object' experiments.
Cost Optimization (Token Economics): Implementing multi-model routing and caching strategies to reduce LLM operational expenditure (OpEx) by up to 40%.
Risk & Compliance Governance: Managing the 'Cost of Failure' through deterministic safety layers and hallucination prevention frameworks.
I translate complex Agentic architectures into clear business outcomes, ensuring that AI initiatives are not just technically sound, but financially defensible at the Board level.
AI ROI Modeling: Designing 12-month roadmaps that prioritize high-impact use cases over 'shiny object' experiments.
Cost Optimization (Token Economics): Implementing multi-model routing and caching strategies to reduce LLM operational expenditure (OpEx) by up to 40%.
Risk & Compliance Governance: Managing the 'Cost of Failure' through deterministic safety layers and hallucination prevention frameworks.
I translate complex Agentic architectures into clear business outcomes, ensuring that AI initiatives are not just technically sound, but financially defensible at the Board level.
The technical foundation for my systems architecture career. This degree provided the core principles of data structures, algorithms, and distributed systems that I now apply to high-scale Agentic AI workflows. It ensures that every AI solution I design is built on a solid, scalable engineering foundation.
- AI Engineer Core Track: LLM Engineering, RAG, QLoRA, Agents 30/01/2026
![AI Engineer Core Track: LLM Engineering, RAG, QLoRA, Agents]()
Advanced specialization in the mechanics of frontier models. I focus on optimizing LLM performance through:
Advanced RAG: Implementing hybrid search and reranking for high-precision context retrieval.
Efficiency: Utilizing QLoRA for memory-efficient fine-tuning when domain-specific performance is required.
Context Management: Mastering long-context window handling to minimize 'lost-in-the-middle' phenomena in enterprise data
- 🇬🇧 English
- 🇮🇳 Hindi
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