PART 1.

CURRICULUM VITAE

Table of Contents

SECTION 1. MY CONTACTS

SECTION 2. JOB CONDITIONS

SECTION 4. PORTFOLIO

SECTION 5. DETAILED WORK EXPERIENCE BY DATE (core CV)

1. • YEARS: 2021 – 2025

     • YEARS: 2019 – 2021

     • YEARS: 2015 – 2018

     • YEARS: 2007 – 2014

SECTION 6. DETAILED PROGRAMMING SKILLS, TECHNOLOGIES, ETC

• python, node, artificial intelligence, cpp, cloud , management

SECTION 7. EDUCATION AND LANGUAGES

SECTION 8. PERSONAL INFO

SECTION 9. COVER LETTER

SECTION4. Portfolio.

Blogs:

hashnode.com/670427ad64970513dc6f29ed/dashb..

https://programmingetc.hashnode.dev/series/frontend-with-angular

https://programmingetc.hashnode.dev/series/javaquarkus

hashnode.com/670427ad64970513dc6f29ed/dashb.. hashnode.com/67241fbc190e1b2c6ef32da7/dashb.. hashnode.com/6724e92342468b6c53acbe2d/dashb.. hashnode.com/6725592109e09d5a67f7e7b2/dashb..

Code:

hub.docker.com/repository/docker/user987987

stackoverflow.com/users/8821437/quine997 github.com/bender876487

console.akash.network/template/25a1421c-dbb.. v2.akord.com/public/vaults/active/EuY04J2uw.. v2.akord.com/public/vaults/active/zdCyxZfbs..

drive.google.com/file/d/1YLy60jPvXOBmZGxqkk.. drive.google.com/file/d/1Z_3QtGI5KfwQgTJ1bA.. drive.google.com/file/d/1PEN9zZ5ynAAZmzf6Hw..

SECTION5. DETAILED WORK EXPERIENCE BY DATE. (core CV)

YEARS: January 2021 – January 2025 .

Titles: Freelance

Main projects etc.

1. Development app with LLM/AI ;

   1. Langchain and LlamaIndex., Haystack ; GPT Indexer; Transformers Pipelines; Weaviate; Pinecone; Milvus

2. Deployments AWS, etc:

Toyota app, dot net; c#; azure;

YEARS 2019 – 2021

Titles: Technical Manager, Developer .

Main projects etc.

1. Stock price prediction/classification; Web API; Scraping; AI

   1. Description:

Classification of stock price based on volatility; value; growth; Generation of Bot trading script with AI/NLP

1. 2. Java: Spring boot 3; Hibernate; Jhipster/React; Tomcat; Micronaut Service; Dropwizard Service; RESTEasy Reactive, GraalVM, MicroProfile,

1. Deployment: Docker, Kubernetes, Azure, etc

3. Recommandation System for E-Commerce; Web API; CRUD.

1. 1. Backend etc:

Java: Spring boot 2; Hibernate; Maven; Dropwizard Service; Jetty HTTP Server; JBoss EAP Server Runtime; Quarkus; Infinispan, Micrometer, Kubernetes/OpenShift

1. 2. Deployments etc:

      1. Azure: Azure Cosmos DB, Azure Kubernetes Service (AKS), Azure Functions, Azure Logic Apps, Azure API Management, Azure DevOps, Azure Storage, Azure Synapse Analytics

1. 1. 2. Pipelines etc: Jenkins;

1. 3. Management: Jira

YEARS 2015-2018

Title: Developer.

Main projects etc.

1. Tutoring junior developer.

2. General Decision Making algorithm: Spark-like software with API

   1. Big Data/Analytics: SciKit, QDA, Pandas, NumPy, MongoDB, PySpark, MatPlotLib, Plotly; Tensorflow.js; Python Data library;

1. 2. Java: Spring boot 2; Maven; Dropwizard Service; JBoss EAP Server Runtime;

1. 3. Azure: Azure Cosmos DB, Azure Kubernetes Service (AKS), Azure Functions, Azure Logic Apps, Azure API Management, Azure DevOps, Azure Storage, Azure Synapse Analytics

YEARS 2007 – 2014.

Title: Technical Manager, Developer. Company Velnet;

Main projects etc.

1. 1. 1. Search Engine and Regex libraries. Tools: Python 2; C/C++ language; Scikit learn;

         2. Web API Java: Spring boot 1; Hibernate; Tomcat; Maven; Jetty HTTP Server;

1. 1. 3. Lexer, Parser, and compiler. Tools: PLY, Python, LLVM; C/C++ language;

SECTION6. DETAILED PROGRAMMING SKILLS, TECHNOLOGIES, ETC

Management.

Technical Manager 30 people (5 teams);

Agile Scrum Master; Domain Driven Design; TDD, BDD,

Jira; Monday.com; Asana; Github owner;

Transformation of the requirements’ list to the technical specifics;

I have worked in cross functional roles to fill the the gap between the IT department and Business department.

Java

Spring boot 1-3; Hibernate; Jhipster/React; Tomcat; Maven; Micronaut Service;

Dropwizard Service; Jetty HTTP Server; JBoss EAP Server Runtime; Hibernate ORM with Panache

RESTEasy Reactive, GraalVM, MicroProfile, Apache Kafka, Mutiny, Keycloak, Infinispan,

Micrometer, Kubernetes/OpenShift

Python/AI/Data Analytics:

Artificial Intelligence/Computer Vision; image/video generator.

OpenCV/CV2, Scikit-Image, Pillow/PIL , MMCV, Keras, OpenVINO, Albumentations, Caffe CNN, SimpleCV, cikit; Tensorflow; SciPY; Armadillo/MLPack/Matlab; FFmpeg; GSL Math;

Crypto++; TensorFlow;

Javascript Node Backend:

Node 1-20; REST API; Webpack; NPM, Babel; Http.Server; Express;

Math.js; Node-FS, Promises/Asynchronous; React.sj; Angular

Azure.

Azure Cosmos DB, Azure Kubernetes Service (AKS), Azure Functions, Azure Logic Apps, Azure API Management, Azure Monitor, Azure Event Grid, Azure DevOps, Azure Storage, Azure Synapse Analytics

Cloud AWS

7. Miscellanea: EC2, S3; Networking, Security, AMI management/migration; CloudWatch; API Gateway; Route53; Cognito; IAM, Lambda; AWS Copilot CLI

3. Devops/Container: Fargate; ECR; ECS, Terraform, Kubernetes

3. AWS Serverless/Lambda: Node, Python; Static S3 Web App;

SECTION7. EDUCATION AND LANGUAGES.

• University: University of Naples Federico II, Physics. Mark 28/30;

• High School: Programming School, Giacomo Giuliani, Mark 100/100;

• Spoken Languages: Native Italian; Professional English C2;

SECTION8. PERSONAL INFO.

• Italian nationality/passaport.

• Current city Caserta: I can move in some case

• Birth year: 1984.

SECTION9. COVER LETTER.

Basically I am a skilled Technical Manager and Fullstack Developer, with 17 years of experience;

I have managed international remote_distributed_cross_functional_teams, with 25-30 professionals.

As developer I have developed: full stack web app, AI software, Big Data Pipelines,

and cloud architecture

I have a background in Physics; Mathematics; Computer Science;

I have the skills to solve a wide range of analytical problem.

I am a fast learner, fast developer, and natural leader.

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PART 2.

10 Technical Questions for CV Evaluation

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1. Cloud & Kubernetes: How would you design a multi-region Kubernetes deployment using AWS?

Technical Answer:

1. Networking & Load Balancing: Use VPC peering, Route53, and AWS Global Accelerator for multi-region traffic routing. Deploy Nginx Ingress Controller and ExternalDNS for global routing.

2. Scaling & Failover: Enable Cluster Autoscaler (CA) and Horizontal Pod Autoscaler (HPA) with metrics from AWS CloudWatch. Use multi-AZ clusters via Amazon EKS.

3. Storage & State Management: Utilize Amazon EFS for persistent storage across regions, Kafka MirrorMaker for log consistency, and multi-region RDS read replicas for high availability.

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2. Machine Learning: How do you handle concept drift in real-time ML models?

Technical Answer:

1. Continuous Monitoring: Implement Kolmogorov-Smirnov tests, Population Stability Index (PSI), and KL divergence to detect statistical drift in incoming data. Use Grafana + Prometheus for alerting.

2. Adaptive Learning Pipelines: Employ sliding window retraining with Apache Airflow, and automate model selection via Bayesian optimization with hyperparameter tuning.

3. Dynamic Feature Engineering: Utilize feature stores (Feast) and online models using Vector DBs (Weaviate, Pinecone, FAISS) to adapt embeddings dynamically.

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3. AI & NLP: How would you fine-tune a large language model for a domain-specific chatbot?

Technical Answer:

1. Dataset Curation: Collect and preprocess domain-specific text using spaCy, NLTK, and Hugging Face datasets. Use BERT Tokenizers and apply TF-IDF filtering.

2. Fine-Tuning Process: Leverage LoRA (Low-Rank Adaptation) with QLoRA on GPT models using Deepspeed for memory efficiency. Train on A100 GPU clusters via AWS Sagemaker.

3. Inference Optimization: Deploy ONNX-optimized models with TensorRT on FastAPI endpoints. Integrate Langchain + FAISS for semantic retrieval in production.

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4. DevOps & CI/CD: How would you set up a GitOps pipeline for a microservices architecture?

Technical Answer:

1. Repository & Branching: Use GitHub Actions to trigger ArgoCD workflows with Helm Charts. Implement monorepo or polyrepo strategy depending on service boundaries.

2. Containerization & Security: Use Docker multi-stage builds, Trivy for vulnerability scanning, and OPA (Open Policy Agent) for RBAC within Kubernetes clusters.

3. Observability & Rollbacks: Utilize Prometheus, Loki, and Grafana dashboards for monitoring. Implement progressive delivery with Flagger + Istio for controlled rollouts.

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5. Big Data: How would you design a real-time analytics pipeline for event processing?

Technical Answer:

1. Ingestion Layer: Use Kafka Streams with KSQLDB for preprocessing. Enable AWS Kinesis Firehose for low-latency streaming into Amazon S3 or Redshift.

2. Processing & Transformation: Leverage Apache Flink or Spark Streaming with Delta Lake for real-time ETL. Implement stateful aggregations via RocksDB-backed state stores.

3. Serving & Querying: Expose data via PrestoDB on AWS Athena. Use Druid or Clickhouse for high-speed analytical queries. Implement OLAP cubes for reporting.

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6. Backend System Design: How would you build a scalable GraphQL API for a high-traffic application?

Technical Answer:

1. Schema Design & Batching: Implement Apollo Federation for modular GraphQL schemas. Enable DataLoader batching to reduce redundant calls to DBs.

2. Caching & Performance: Use Redis Edge Caching, GraphQL persisted queries, and PostgreSQL with Citus extension for horizontal scaling.

3. Security & Rate Limiting: Implement JWT-based authentication, enforce query cost analysis, and deploy GraphQL Depth Limit rules to prevent expensive queries.

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7. Distributed Systems: How do you ensure consistency in a globally distributed NoSQL database like DynamoDB?

Technical Answer:

1. Consistency Models: Use Strongly Consistent Reads for critical transactions, Eventual Consistency for non-critical queries, and DynamoDB Streams + Lambda for cross-region replication.

2. Partitioning & Sharding: Use adaptive capacity units with Partition Keys and LSI/GSIs for read-heavy workloads.

3. Conflict Resolution: Implement CRDTs (Conflict-Free Replicated Data Types) for concurrent updates and leverage vector clocks for last-write wins resolution.

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8. Security: How would you secure a cloud-native application running on AWS?

Technical Answer:

1. IAM & Least Privilege: Implement IAM policies with service roles. Use STS (Security Token Service) with OIDC to avoid long-lived credentials.

2. Secrets Management: Store secrets in AWS Secrets Manager, use KMS for key encryption, and ensure VPC peering for isolated networking.

3. Runtime Security: Enforce runtime scanning (Falco, Sysdig) and deploy WAF rules to prevent SQL injection & XSS attacks.

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9. High-Performance Computing: How do you optimize deep learning model training for speed and memory efficiency?

Technical Answer:

1. Data Parallelism: Use Horovod with NCCL backend for multi-GPU training. Implement Mixed Precision Training (AMP) for lower memory consumption.

2. Memory Optimization: Apply gradient checkpointing to reduce RAM overhead, use TFRecord datasets with Prefetching + Shuffling for faster I/O.

3. Distributed Training: Leverage FSDP (Fully Sharded Data Parallel) + DeepSpeed for massive model scaling across TPU/GPU clusters.

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10. Web3 & Blockchain: How would you implement a smart contract-based authentication system?

Technical Answer:

1. Identity Management: Use Ethereum Smart Contracts (Solidity) + OpenZeppelin for role-based authentication. Deploy via Truffle + Hardhat.

2. Signature-Based Login: Implement EIP-712 signatures for off-chain authentication to avoid high gas fees, storing user identity securely on IPFS.

3. On-Chain & Off-Chain Data Sync: Use Chainlink Oracles to sync off-chain KYC data securely, ensuring privacy compliance with zk-SNARKs.

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Final Evaluation Approach:

✅ Each question validates expertise in multiple domains (Cloud, AI, DevOps, Web3, Security).
✅ Answers demonstrate depth in system design, performance, and scalability.
🔹 Further improvements: Detail multi-cloud architecture, API rate limiting, and advanced ML optimization techniques.