Sampath Valluri
ML Engineer
4+ years exp
4+ years ML exp
Bash
Node JS
Python
SQL
JavaScript
PHP
TypeScript
Active 8 days ago
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Overview
Technical skills
Timeline
Roles
Overview
Backend engineer (approx mid-level) focused on serverless AWS integrations and web backends with a pragmatic, hands-on delivery style. The strongest proven skill is integrating AWS services into working pipelines, as shown by the Python Lambdas using Rekognition/Textract/S3/DynamoDB and the Java CloudFormation orchestration tool. The public code lacks robust production hardening evidence such as structured observability, resilient retry/backoff patterns, and consistent secure input handling.
Technical skills
Bash
Node JS
Python• Middle • 4y+
SQL• Middle • 4y+
JavaScript• Junior
PHP• Middle
TypeScript
Node JS
Express
Python
Django
FastAPI• 4y+
Flask
Databases
Databricks
Delta Lake
FAISS
pgvector
Pinecone
PostgreSQL
Qdrant
DynamoDB
Oracle
Frontend
Bootstrap
React.js• 4y+
DevOps
AWS
GCP
Git
Vector
CloudFormation
CI/CD
Cloud
Amazon EC2• 4y+
AWS Lambda• 4y+
CI/CD• 4y+
Docker• 4y+
GitHub Actions• 4y+
Rest API• 4y+
Kubernetes
QA
Postman
AI/ML
DeepEval
Hallucination
LangGraph
LlamaIndex
LLM
Ragas
Transformers
Embeddings• 4y+
LangChain• 4y+
Prompt Engineering• 4y+
Semantic Search• 4y+
Anomaly Detection
MLFlow
PyTorch
RAG
Spark
Cybersecurity
Auth0
Timeline
Software Developer (AI/ML)
•
Middle
Databricks
•
Full-Time
Architected fault-tolerant data pipelines processing large structured and unstructured datasets using Python, Apache Spark, and SQL. Built and productionized anomaly detection models for high-volume event streams and improved detection quality and operational efficiency. Designed RAG pipelines using LLM embeddings and owned the end-to-end MLOps lifecycle for multiple production ML and GenAI services, including deployment automation and monitoring. Developed low-latency FastAPI inference services and contributed across product and security teams, mentoring engineers and delivering production AI features.
Python
Spark
SQL
PyTorch
Anomaly Detection
RAG
Embeddings
FastAPI
Rest API
Docker
Kubernetes
MLFlow
CI/CD
University of Central Oklahoma
Master's Degree •
Computer Science
Software Developer
•
Middle
Wipro
•
Full-Time
Led end-to-end development of an AI-powered web platform using Python, FastAPI, and REST APIs, improving latency and accelerating feature delivery for daily users. Implemented responsive front-end functionality and built GenAI capabilities using LLMs with LangChain, prompt engineering, and embeddings. Developed RAG pipelines with semantic search and document chunking approaches to improve retrieval accuracy, and optimized SQL-backed data models for analytics. Containerized services with Docker, deployed on AWS (EC2 and Lambda), and implemented CI/CD with GitHub Actions to increase release frequency and testing quality.
Pythonsince 2022
FastAPIsince 2022
Rest APIsince 2022
React.js
LangChain
Prompt Engineering
Embeddingssince 2022
Semantic Search
SQLsince 2022
Dockersince 2022
Amazon EC2
AWS Lambda
GitHub Actions
CI/CDsince 2022
Middle Backend Developer
Confidence: Medium API Engineer
Backend engineer (approx mid-level) focused on serverless AWS integrations and web backends with a pragmatic, hands-on delivery style. The strongest proven skill is integrating AWS services into working pipelines, as shown by the Python Lambdas using Rekognition/Textract/S3/DynamoDB and the Java CloudFormation orchestration tool. The public code lacks robust production hardening evidence such as structured observability, resilient retry/backoff patterns, and consistent secure input handling.
API Design
3/10
How well APIs are designed
Basic API handling with Lambda entrypoints, simple error contracts and CORS; no versioning, idempotency keys, or standardized error schema.
Evidence
sampath1247/cloud-iac-cicd/iac/backend/lambda_function.py: lambda_handler input parsing and error responses
sampath1247/participation-record-serverless-aws/serverless-participation-aws/backend/lambda_function.py: lambda_handler API payload handling and response
sampath1247/cloud-iac-cicd/iac/frontend/proj3_deployment_lambda.py: S3-triggered lambda that acts as an API for starting Amplify deployments
Data Layer & Database
3/10
Working with databases
Working with multiple datastores (DynamoDB and Oracle); uses stored procedures and bind variables in places but lacks explicit migration history and advanced query tuning.
Evidence
sampath1247/cloud-iac-cicd/iac/backend/lambda_function.py: table.put_item to DynamoDB
sampath1247/participation-record-serverless-aws/serverless-participation-aws/backend/lambda_function.py: writes to DynamoDB
sampath1247/student-enrollment-system-php/student-enrollment-system/student_add_action.php: oci_bind_by_name and stored procedure call to insert_student
Scalability & Performance
3/10
Handling load and speed
Uses event-driven serverless patterns and S3-triggered deployments which provide decoupling; lacks caching strategies, connection pooling, rate limiting, or measured performance optimizations.
Evidence
sampath1247/cloud-iac-cicd/iac/frontend/proj3_deployment_lambda.py: S3 event -> Amplify deployment
sampath1247/cloud-iac-cicd/cicc-sdk-demo/src/main/java/edu/uco/cicc/CloudFormationDemo.java: sets S3 event notification and uploads artifacts
sampath1247/participation-record-serverless-aws/serverless-participation-aws/backend/lambda_function.py: scalable serverless pattern using S3 + Rekognition/Textract
System Architecture
3/10
Overall system structure
Clear separation between frontend and backend lambdas and IaC use shows deliberate decomposition; overall architecture is straightforward without service contracts, resilience tiers, or multi-service orchestration beyond simple orchestration scripts.
Evidence
sampath1247/cloud-iac-cicd/cicc-sdk-demo/src/main/java/edu/uco/cicc/CloudFormationDemo.java: orchestration of backend/frontend/amplify stacks
sampath1247/cloud-iac-cicd/iac/backend/lambda_function.py: backend lambda implementing business logic separate from deployment lambda
sampath1247/cloud-iac-cicd/iac/frontend/proj3_deployment_lambda.py: separate deployment lambda for frontend
Security & Auth
2/10
Protecting data and access
Some security awareness in S3 policy construction and use of bind variables in Oracle insertion logic, but there are multiple insecure patterns such as string-concatenated SQL, CORS '*' and hard-coded identifiers/credentials.
Evidence
sampath1247/cloud-iac-cicd/cicc-sdk-demo/src/main/java/edu/uco/cicc/CloudFormationDemo.java: builds S3 bucket policy including aws:SecureTransport deny condition
sampath1247/student-enrollment-system-php/student-enrollment-system/admin.php: dynamic SQL WHERE clauses built via string concatenation (SQL injection risk)
sampath1247/cloud-iac-cicd/iac/frontend/proj3_deployment_lambda.py: responses with CORS '*' header and hard-coded Amplify appId in params
Reliability & Observability
2/10
Stability and monitoring
Basic error handling and logging exist; there is minimal observability, no structured tracing/correlation ids, and no retry/backoff or graceful shutdown patterns.
Evidence
sampath1247/cloud-iac-cicd/cicc-sdk-demo/src/main/java/edu/uco/cicc/CloudFormationDemo.java: uses SLF4J logger and try/catch logging
sampath1247/cloud-iac-cicd/iac/backend/lambda_function.py: try/except around input parsing and AWS calls with printed errors
sampath1247/participation-record-serverless-aws/serverless-participation-aws/backend/lambda_function.py: prints and basic exception handling for Rekognition/Textract calls
Verified artifacts
Expertise
Microservices & API Architecture• Middle
Databases & Vector Storage• Middle
Industries
Education• Middle
Technologies
PHP• Middle
Oracle
DynamoDB
CloudFormation
MySQL• mentioned only
Stack• mentioned only
Recommendations
- Develop event-driven backend features that integrate AWS managed services (Lambda, S3, Rekognition/Textract, DynamoDB) and small orchestration tooling for deployments.
- Harden existing systems by adding input validation, parameterized queries across all PHP code, secrets management, and removing hard-coded credentials/IDs.
- Implement basic observability and resilience: structured logs with correlation ids, retries with exponential backoff for AWS calls, and lightweight metrics/alerts.
- Work on backend-for-frontend APIs and serverless CI/CD flows where rapid prototyping and integrations are required
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
Middle QA Engineer
Confidence: Medium Generalist
A cloud and systems-focused generalist developer at a middle level with practical experience building small serverless integrations and host automation. The strongest proven skill is integrating AWS services into a working serverless handler that ingests uploads and calls Rekognition and Textract to derive business logic for participation verification. There is little to no evidence of automated tests, CI integration, contract testing, or formal QA artifacts in public code.
Test Automation Frameworks
1/10
Building automated tests
Minimal test automation artifacts; some defensive coding in handlers but no fixtures, test harness, or automation framework present.
Evidence
participation-record-serverless-aws/serverless-participation-aws/backend/lambda_function.py: input parameter validation and try/except around parsing and AWS calls
ec2-s3-cron-file-sync-java/ec2-s3-cron-file-sync-java/java/FileSyncLogger.java: logging and helper methods that could be tested but no test files exist
Test Coverage & Strategy
1/10
What and how to test
No test suites, risk-based test tagging, or property/negative-path test coverage visible; limited negative-path handling exists in code but not encoded as tests.
Evidence
participation-record-serverless-aws/serverless-participation-aws/backend/lambda_function.py: returns 400 for missing parameters and 500 on DynamoDB write failure
virtualbox-cpu-autoscaler-java/virtualbox-cpu-autoscaler-java/src/p04.java: exception handling around external command execution and file parsing
API & Integration Testing
2/10
Testing how parts work together
Direct integration code with external services (AWS SDK) is present and some runtime error handling exists, but there is no contract or schema testing, mocks, or idempotency tests.
Evidence
participation-record-serverless-aws/serverless-participation-aws/backend/lambda_function.py: uses boto3 clients for Textract, Rekognition, S3, and DynamoDB with try/except around detect/compare calls
ec2-s3-cron-file-sync-java/ec2-s3-cron-file-sync-java/java/FileSyncLogger.java: writes local files intended to be synced to S3 via cron (integration pattern but no test harness)
Performance & Load Testing
2/10
Testing speed under load
Some performance-oriented system code and a synthetic load generator exist, but there are no structured load tests, thresholds, or percentile analysis artifacts.
Evidence
virtualbox-cpu-autoscaler-java/virtualbox-cpu-autoscaler-java/src/p04.java: elasticity control logic using Idle% thresholds to plug/unplug CPUs
virtualbox-cpu-autoscaler-java/virtualbox-cpu-autoscaler-java/src/p04_load.java: synthetic CPU load generator for demoing elasticity
Bug Reporting & Analysis
1/10
Finding and describing bugs
Basic debugging traces and console logging are present, but there are no formal bug reports, root-cause analyses, or reproducible issue artifacts.
Evidence
virtualbox-cpu-autoscaler-java/virtualbox-cpu-autoscaler-java/src/p04.java: System.err and thrown IOException handling with messages for failing external commands
ec2-s3-cron-file-sync-java/ec2-s3-cron-file-sync-java/java/FileSyncLogger.java: use of e.printStackTrace and log.dat for runtime tracing
CI Test Integration
Running tests automatically
Not evidenced in public code
Industries
Education• Middle
Software• Middle
Technologies
Cloud
Postman
AWS
AWS Lambda• 4y+
Amazon EC2• 4y+
Java• mentioned only
VirtualBox• mentioned only
Recommendations
- Develop and harden serverless REST APIs and integrations that call cloud services (Lambda handlers, S3/DynamoDB patterns).
- Build automation tools and host-side controllers for virtualization and orchestration (hotplug controllers, cloud VM tooling).
- Add test infrastructure and CI: unit and integration tests for Lambda handlers, mocks for AWS SDK calls, and pipeline automation to validate negative/error paths.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
Intern Frontend Developer
Confidence: Low Generalist
Early-career frontend generalist focused on delivering basic web UI assets and small CI/CD demos. The strongest proven evidence is integration of standard frontend libraries such as Popper and Bootstrap visible at wwwroot/lib/popper.js and wwwroot/lib/twitter-bootstrap/css/bootstrap-grid.rtl.css. There is little to no original application logic, custom components, async state handling, tests or accessibility/performance work evidenced in the public files.
UI Component Architecture
How interface parts are built
Not evidenced in public code
Responsive & Cross-browser
Works on all screens and browsers
Not evidenced in public code
Performance Optimization
Speed of the interface
Not evidenced in public code
Accessibility & Semantics
Usable for everyone
Not evidenced in public code
State Management & Data Flow
Managing data in the app
Not evidenced in public code
UX & Visual Polish
Look and feel quality
Not evidenced in public code
Expertise
HTML & CSS• Intern
Frontend Architecture & Build Tools• Intern
Technologies
CI/CD
JavaScript• Junior
TypeScript
Node JS
Express
GitHub Actions• 4y+
Bootstrap
React.js• 4y+
Recommendations
- Maintain and integrate UI libraries and layout work using Bootstrap and small vanilla JavaScript enhancements.
- Implement or integrate simple CI/CD pipelines and static deploy demos using GitHub Actions and AWS for basic automation.
- Work on small UI feature tasks or migrations that require bundling vendor libraries and verifying cross-browser behavior.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories: