I am an AI Engineer based in Bengaluru, India, with experience building machine learning applications, generative AI systems, backend services, and full-stack products.

My work focuses on the intersection of:

• Machine learning and deep learning
• Generative AI, RAG, and AI agents
• MLOps and production model deployment
• Backend and cloud-native engineering
• Developer automation and intelligent tooling

I enjoy taking AI ideas beyond experimentation and turning them into reliable, usable systems.

• Building production-ready machine learning and generative AI applications
• Designing RAG pipelines, AI agents, and tool-using workflows
• Deploying models through APIs, containers, and cloud infrastructure
• Learning advanced MLOps, Kubernetes, distributed ML systems, and GPU programming
• Improving model monitoring, evaluation, retraining, and deployment workflows

An automated code-review system that analyses pull requests, retrieves related Jira or Azure Boards context, and generates both summary-level and file-level feedback.

Highlights

• GitHub Actions and GitHub App integrations
• Azure OpenAI-powered code analysis
• Jira and Azure Boards work-item retrieval
• Inline review comments and pull-request summaries
• Context-aware responses to mentions and review discussions

A multimodal interview assessment platform that evaluates candidate responses using audio, video, natural language processing, and computer vision.

Highlights

• Speech transcription and filler-word analysis
• Response relevance scoring
• Eye-contact, posture, smile, and confidence assessment
• React and Next.js frontend
• Python-based machine learning services
• Dockerized deployment architecture

A machine-learning pipeline for identifying anomalous operating conditions in automotive and equipment sensor data.

Highlights

• Autoencoder-based feature extraction and anomaly scoring
• XGBoost classification
• Time-series and sensor-data preprocessing
• Separate modelling strategies for different equipment types
• Interactive visualisation and CSV inference interface

A tool-using AI agent for reading and creating Jira issues from structured and unstructured input.

Highlights

• LangGraph and ReAct-based workflows
• Epic, user-story, and subtask creation
• Excel-driven bulk issue generation
• Human-in-the-loop confirmation
• Issue updates, comments, assignment, and linking

A cloud data architecture for ingesting, transforming, storing, and serving data for machine learning inference.

Highlights

• Azure Data Lake Storage
• Databricks and Delta Lake
• Medallion architecture
• Streaming and batch transformation pipelines
• Model training and inference workflows

• Machine learning and deep learning
• Natural language processing
• Computer vision
• Time-series anomaly detection
• Retrieval-augmented generation
• AI agents and tool calling
• Embeddings and semantic search
• Model evaluation and inference

• REST API design
• Authentication and authorization
• Microservices
• Asynchronous processing
• AI model serving
• Third-party API integrations

• Docker and Kubernetes
• CI/CD pipelines
• Azure App Service, Functions, VMs, Storage, and networking
• AWS data engineering services
• Model deployment and monitoring
• Databricks, Delta Lake, and data pipelines
• Linux server administration

I am interested in opportunities and collaborations involving:

• Machine learning engineering
• MLOps and ML platforms
• Generative AI and RAG
• AI agents and developer tooling
• Computer vision and NLP
• Backend engineering for AI systems
• GPU programming and ML systems research

• Portfolio: sonythomas.me
• LinkedIn: linkedin.com/in/njansony
• GitHub: github.com/STSonyThomas
• X: x.com/sony_national