Shreyas Shashi Kumar Gowda
AI Systems Architect | Agentic & Temporal AI | Engineering Verifiable Intelligence at System Scale |
Engineering the boundary where abstract intent converts to deterministic execution.
Architecting AI systems designed for post-deployment correctness. The focus lies in agentic architectures, temporal reasoning, and graph-based retrieval—prioritizing verifiable control and cost-efficiency over optical demos.
This work bridges research capabilities with production constraints, translating raw intelligence into governable, industrial-grade systems. Principled engineering. Verifiable results.
This section intentionally blends jobs and system-level work. It reads as ownership, not employment history.
- Evaluation Architecture: Developed modular evaluation frameworks to quantify generative model outputs beyond surface metrics, establishing objective quality standards.
- Data Strategy: Directed targeted dataset enrichment strategies to identify and resolve coverage gaps in model training data.
- Scalability: Standardized reusable rubric templates across distinct prompt types, increasing evaluation consistency and throughput for production pipelines.
Focus: evaluation reliability, model quality control, and scalable assessment pipelines.
- Alpha Modeling: Engineered and validated high-impact alpha models within a production environment utilizing advanced statistical methodologies.
- Signal Extraction: Analyzed complex datasets to isolate decorrelated signals, contributing to a scalable and robust alpha library.
- Performance Optimization: Refined signal logic through iterative stress-testing, yielding a 37% improvement in backtested Sharpe ratios.
Focus: mathematical rigor, signal robustness, and performance-driven iteration.
How I Build Systems
System fragility rarely stems from model capability. It stems from ambiguous intent, temporal inconsistency, and unconstrained execution.
My work addresses these failure modes directly. To counter silent drift and ungoverned behavior, architecture prioritizes:
- Temporal Reasoning: Maintaining continuous, time-aware context rather than relying on isolated snapshots.
- Governed Agency: Controlling agent behavior through explicit policy layers and defined escalation paths.
- Rigorous Evaluation: Distinguishing verifiable correctness from stochastic coincidence.
- Deterministic Orchestration: Constraining probabilistic model outputs within deterministic execution logic.
Research informs the theory; engineering enforces the reliability.
These projects exist as responses to real failure patterns, not as portfolios.
ChronoRAG
Temporal Retrieval-Augmented Generation Architecture:
Implements explicit valid-time and transaction-time reasoning within RAG pipelines.
Mechanism: Enforces historical consistency during retrieval and synthesis to eliminate temporal hallucinations.
Optimization: Integrates temporal guards and cost-aware query routing for scalable, low-latency performance.
Core Value: Ensuring longitudinal correctness over point-in-time accuracy.
TimeGuard GraphRAG
Graph-based RAG with temporal and policy constraints
Architecture: Couples graph traversal with strict time-gated access paths.
Mechanism: Filters invalid reasoning by enforcing temporal validity on node associations during retrieval.
Application: Designed for governed enterprise environments requiring bounded, verifiable output.
Core Value: Structured reasoning enforced by system constraints.
SPL Framework
Subsumption Pattern Learning for Agent Control
- Architecture: Layered control framework prioritizing deterministic validation over probabilistic generation.
- Mechanism: Orchestrates multi-layer decision flows (L0–L2) utilizing pattern distillation and confidence gating.
- Optimization: Minimizes latency and token consumption while maximizing execution reliability.
- Core Value: Efficient escalation logic essential for scale.
Closing line Constructed to intercept silent failure modes before they cascade.
Bachelor of Engineering — Computer Science
BMS Institute of Technology & Management, Bengaluru
Dec 2022 – Jun 2026
Bachelor of Science — Data Science
Indian Institute of Technology Madras
Jan 2023 – Jul 2025
This section is compact but dense.
Programming
Python, C++
Technical Stack & Focus
Foundational AI & ML
- Deep Architecture: Leveraging Transformers, attention mechanisms, and representation learning to engineer systems from first principles, rather than relying solely on high-level APIs.
- Advanced Retrieval: Implementing complex RAG and GraphRAG architectures where context is engineered, not just fetched.
- Quantitative Rigor: Applying reinforcement learning (RLHF) and quantitative modeling to enforce statistical reliability in large-scale data processing.
Specialization
- Agentic Architecture: Designing autonomous systems governed by pattern-driven frameworks (SPL) to ensure behavior remains bounded and predictable.
- Temporal Intelligence: Architecting time-aware knowledge systems that distinguish between historical fact and current validity.
- Deterministic Execution: Building pipelines where critical decision nodes are deterministic, using models only for semantic flexibility.
- Production Orchestration: optimizing model orchestration for strict cost and latency budgets, prioritizing operational viability over raw capability.
- Strategic collaboration with engineering organizations on agentic architecture, evaluation methodology, and system governance.
- Engagements are high-leverage, confidential, and focused strictly on deployment outcomes rather than theoretical consulting.
- Commercial context and operational details are maintained under Syperith.
For roles and engineering discussions
job@sskg.syperith.com
For advisory, system design reviews, and private consultations
apex@apex.syperith.com