Open to Senior Data, AI/ML & GenAI Engineering Roles

Pavan Sai Rayalla

Senior Data & AI Engineer building governed GenAI, RAG, agentic AI, and Microsoft Fabric platforms.

I design and ship production AI systems that connect LLMs, retrieval, agents, enterprise data pipelines, APIs, governance, and observability into reliable workflows for regulated business teams.

View AI Platforms System Design Experience GitHub
10+
Years Experience
14
Regulatory Domains
40%
Review Cycle Reduction
200+
Stakeholders Supported

About Me

From trusted data
to production AI

I build AI systems where reliability matters: regulated workflows, enterprise knowledge, sensitive data, audit trails, and human review. My work connects data engineering, retrieval, model orchestration, APIs, and governance into practical platforms that teams can use every day.

I am strongest at taking an AI idea from prototype to production, including the data foundation, grounding layer, agent workflow, evaluation process, monitoring, and release controls needed for real business adoption.

My focus: Building AI that is useful, measurable, traceable, and safe enough for enterprise teams to trust in high-stakes environments.
Data-to-AI Platforms Enterprise RAG Agent Workflows AI Evaluation Human Review Production Delivery
0%
Reduction in manual regulatory review cycles through governed GenAI and multi-agent workflows
0
Corporate Finance and regulatory domains supported through UBS Data and AI modernization
0%
Reduction in manual preprocessing using structured evidence extraction pipelines
0+
Corporate Finance and regulatory stakeholders supported with analytics and self-service insights

Selected Work

AI Engineering Projects

Hands-on builds across evaluation, AgentOps, retrieval, governed analytics, and engineering automation.

AI Governance

ARGUS

↗

LLM red-team, guardrail, and model-risk evaluation platform with app profiles, attack suites, policy findings, reproducible reports, deterministic CI gates, and audit records.

PythonFastAPINext.jsPostgreSQLGuardrailsCI Gates
AgentOps

ORION

↗

Enterprise AI AgentOps control plane for agent registry, traces, tool-call history, approval gates, risk scoring, permission management, and immutable audit logs.

TypeScriptNext.jsFastAPIPostgreSQLRedisAgentOps
Retrieval Intelligence

CITADEL

↗

Evidence-first document intelligence platform with hybrid retrieval, graph-aware expansion, reranking, strict citations, eval gates, and operator-grade visibility.

RAGOpenSearchQdrantNeo4jFastAPIDocker
AI Analytics

HELIOS

↗

Governed AI analytics engineering platform for semantic NL-to-SQL, trust-aware execution, explainable insights, and analytics workflows with typed APIs.

NL-to-SQLAnalyticsFastAPIDuckDBNext.jsGovernance

System Design

Platform Approach

A practical delivery pattern for reliable retrieval, governed agents, measurable quality, and deployable service boundaries.

Platform Flow

1
Ingest documents, events, prompts, traces, and operational metadata.
2
Plan retrieval or agent workflows with explicit routing and access boundaries.
3
Run hybrid retrieval, tool calls, eval suites, or analytics execution through typed APIs.
4
Apply guardrails, approvals, risk scoring, citations, and audit logging before output.

Reusable Stack

Control Surface
Next.js, TypeScript, shadcn-style UI
API Layer
FastAPI, Pydantic, typed service contracts
Retrieval
Vector, BM25, graph expansion, rerankers
Governance
Policies, approvals, red-team evals, CI gates
Persistence
PostgreSQL, Redis, Qdrant, Neo4j, OpenSearch
Delivery
Docker Compose, local-first dev, cloud-ready APIs

Work History

10+ Years of Enterprise Impact

A decade of delivering production AI across finance, healthcare, and retail at scale.

UBS
2023 – Present
Nashville, TN
Senior Data & AI Engineer
Financial Services · Generative AI · Compliance

Leading the architecture of enterprise-grade multi-agent LLM systems for regulatory compliance and risk management across 14 financial domains. Delivered an AI-native assistant suite on Azure OpenAI + LangGraph, enabling analysts to navigate complex regulations with 40% less manual review effort.

40% ↓ manual review 99.95% uptime SLA 14 regulatory domains 50% faster tool onboarding
LangGraphAzure OpenAIPineconeAKSDatabricksReactJSLangSmith
Senior Data & AI Engineer
Healthcare · Clinical NLP · HIPAA Compliance

Built a HIPAA-compliant clinical AI platform serving 30+ hospitals, enabling patient-facing conversational agents and internal clinical NLP workflows. Reduced information retrieval time by 25–40% for clinical staff, with a 40% lower mean-time-to-resolution on system incidents.

25–40% faster info retrieval 40% ↓ MTTR HIPAA compliant
FAISSHugging FaceSageMakerspaCyFHIR / HL7Python
Banner Health
2022 – 2023
Phoenix, AZ
BCS Financial
2021 – 2022
Oakbrook Terrace, IL
Senior Machine Learning Engineer
InsurTech · Fraud Detection · Document AI

Designed end-to-end ML pipelines for insurance fraud detection and intelligent document processing, cutting manual claims handling by 45%. Deployed XGBoost and OCR/NER pipelines on Azure with MLflow-tracked model governance, achieving 92% fraud detection accuracy.

45% ↓ manual processing 92% fraud detection accuracy 35% faster batch reports
XGBoostMLflowSnowflakeAzure FunctionsOCR / NERPython
Senior Data Scientist
Retail / CPG · Personalization · Demand Forecasting

Delivered personalization at massive scale for 60M+ households across 2,800+ Kroger stores. Built promotion optimization and demand forecasting models using XGBoost and ARIMA, improving promotional ROI by 18% and forecast accuracy by 28% via real-time GCP Pub/Sub pipelines.

18% promo ROI uplift 60M+ households 28% ↑ forecast accuracy
XGBoost / ARIMAPySparkDatabricksGCP Pub/SubC# .NET
Kroger
2019 – 2021
Cincinnati, OH
Elind Computers
2015 – 2019
Bangalore, India
Data Scientist
Big Data · Streaming · Predictive Analytics

Developed real-time streaming infrastructure and ML pipelines for behavioral analytics, churn prediction, and IoT event processing at 500K+ events/second. Optimized Spark jobs by 45% using advanced partitioning strategies and achieved 88% churn prediction accuracy with Spark MLlib.

500K+ events/sec 45% faster Spark jobs 88% churn accuracy
KafkaPySparkHadoop / HiveSpark MLlib

Core Skills

Focused on production AI systems: models, retrieval, agents, data pipelines, APIs, and governance.

10+
Years delivery
5
AI platform domains
Cloud
Azure, AWS, GCP
01

Generative AI Systems

Designing LLM applications with prompt strategy, routing, evaluations, guardrails, observability, and production release discipline.

Azure OpenAIOpenAIAnthropicLlamaGemini
02

Agentic Workflows

Building multi-step assistants with tool use, stateful execution, approval gates, human review, traceability, and failure handling.

LangGraphLangChainAutoGenCrewAISemantic Kernel
03

Retrieval & Knowledge Platforms

Creating evidence-first RAG systems with hybrid search, graph expansion, reranking, citation quality checks, and feedback loops.

GraphRAGQdrantPineconeNeo4jOpenSearchpgvector
04

MLOps, Data & Delivery

Taking models from experimentation into governed services with pipelines, feature flows, monitoring, CI/CD, and cloud infrastructure.

DatabricksMLflowPySparkKafkaDockerKubernetes

Credentials

Professional Certifications

Verified expertise across AI, cloud, and data engineering platforms.

Microsoft
Microsoft Azure
AI-102: Azure AI Engineer Associate
✓
Microsoft
Microsoft Azure
AZ-305: Azure Solutions Architect Expert
✓
Microsoft
Microsoft Fabric
DP-700 / DP-600: Fabric Data & Analytics Engineer
✓
Anthropic
Anthropic
Claude Certified Architect
★
Databricks
Databricks
Databricks Certified ML Professional
✓
Snowflake
Snowflake
SnowPro Specialty: Gen AI
✓
Snowflake
Snowflake
COF-C03: SnowPro Core Certification
✓
AWS
Amazon AWS
DVA-C02: AWS Certified Developer Associate
✓

Get In Touch

Let’s Talk About
Production AI

Currently open to Senior AI/ML opportunities
Email
pavansair.work@gmail.com
Phone
(469) 619-5673
Location
United States of America
Start a Conversation
Share the role, platform challenge, or AI system you want to discuss. I usually respond within 24 hours.

Your information is kept private and never shared.