Our technology stack

We use proven tools and platforms to deliver robust, scalable data solutions.

Python

Our primary language for data engineering: batch and streaming pipelines, ETL jobs, and automation. We combine libraries such as Pandas and PySpark with frameworks like Airflow to orchestrate reliable workflows. Python also underpins our APIs, utilities, and glue code between systems.

Java

Used for high-throughput, long-running services where performance and type safety matter. We build and maintain Java- and Spring-based components that power data ingestion, business logic, and integration with legacy platforms. This lets us evolve existing ecosystems without rewriting everything from scratch.

PySpark / Spark

Our core engine for big data, enabling distributed processing of large datasets. With PySpark and Spark SQL we implement batch transformations, streaming jobs, and feature pipelines that scale horizontally. This is the backbone for many of our analytics and ML workloads in the cloud.

CI/CD

We treat data pipelines like software and ship them through automated CI/CD. Build and test stages validate code, run quality checks, and enforce conventions before changes reach production. This reduces regressions and makes releases predictable and auditable.

Kubernetes

Used as a platform for running containerised data services and workloads. We deploy Spark jobs, orchestration tools, and supporting APIs onto Kubernetes clusters for resilience and autoscaling. This gives us consistent operational patterns across environments.

Docker

We package pipelines, services, and tools into Docker images so they behave the same on laptops, CI, and production. This removes 'works on my machine' issues and simplifies rollbacks and blue‑green deployments. It also helps standardise dependencies across teams.

Databricks

Our preferred platform for lakehouse architectures, combining notebooks, jobs, and Delta Lake. We use it to develop, schedule, and monitor Spark workloads and collaborative analytics. Its governance and performance features help keep large data platforms maintainable over time.

Microsoft Azure

We design solutions around Azure services such as Data Factory, Synapse, Storage, and AKS. This lets us connect ingestion, processing, storage, and analytics into one cloud-native platform. We focus on cost-awareness, security, and ease of operations on Azure.

SQL & Data Warehousing

SQL and data modeling remain central to how we shape data for analytics. We design schemas, dimensional models, and warehouse structures that are easy to query and govern. This underpins dashboards, self-service BI, and data products across the business.

Data Governance

We embed quality checks, lineage tracking, and access control directly into pipelines and platforms. This helps teams trust the data they consume and understand how it was produced. Governance is implemented as code where possible, not just as documentation.

Machine Learning

We build end-to-end ML pipelines: data preparation, training, evaluation, and deployment. Our focus is on reproducible experiments and MLOps practices so models can be monitored and iterated safely. This turns prototypes into sustainable, production-grade solutions.

AI & NLP

We apply AI and natural language processing to extract value from text and unstructured data. That can mean search and ranking, document understanding, or integration of large language models. We always align these solutions with clear business outcomes and responsible use of AI.

Computer Vision

We use computer vision to analyse images and video for quality control, automation, and insights. Models can run in the cloud or closer to devices, depending on latency and data constraints. These solutions are integrated with existing data platforms so vision outputs become part of the wider analytics picture.

MLflow

We use MLflow for comprehensive experiment tracking, model versioning, and lifecycle management. Our ML workflows leverage MLflow to log parameters, metrics, and artifacts from training runs, enabling reproducibility and comparison across model iterations. We integrate MLflow with Databricks for seamless experiment orchestration and model registry governance.

Unity Catalog

We implement Databricks Unity Catalog to enforce fine-grained access control, lineage tracking, and data governance across ML pipelines and model assets. Unity Catalog provides a centralized, secure model registry where we can version, stage, and deploy ML models with audit trails and compliance assurance. This is critical for production ML systems that handle sensitive data.

Deep Learning & Neural Networks

We build and train neural networks for complex pattern recognition and feature learning. Our expertise spans convolutional networks (CNNs) for image classification and analysis, recurrent networks (RNNs/LSTMs) for sequential data, and attention mechanisms for modern architectures. We implement distributed training on GPU clusters to handle large datasets efficiently.

PyTorch

PyTorch is our framework of choice for research-oriented and production ML work, especially in computer vision and custom architectures. We use PyTorch's dynamic computation graphs for flexible model design, and combine it with tools like Torchvision for transfer learning and pretrained models. PyTorch integrates seamlessly with Databricks for distributed training.

Image Classification & Computer Vision Pipelines

We design end-to-end image classification systems from raw image data to production models. This includes data preprocessing, augmentation, feature extraction, and transfer learning with pretrained models (ResNet, EfficientNet, Vision Transformers). We handle scale challenges via distributed data loading and multi-GPU training on Databricks.

Feature Stores & Feature Engineering

We architect feature stores to centralize feature computation, versioning, and serving for ML pipelines. Using Databricks' Delta Lake as the foundation, we build scalable, low-latency feature platforms that ensure training and serving consistency. This includes feature lineage, time-travel capabilities, and integration with both batch and real-time ML workflows.

MLOps & Model Deployment

We implement end-to-end MLOps frameworks ensuring models move safely from development to production. This includes automated model evaluation, A/B testing, canary deployments, and rollback mechanisms. We use Databricks Model Serving, batch inference jobs, and API endpoints to operationalize models at scale with monitoring and retraining pipelines.

Delta Lake

Delta Lake provides ACID transactions and data versioning for our data lakes, making them as reliable as data warehouses. We use Delta for feature stores, training data versioning, and model pipeline outputs. Its time-travel and schema inference capabilities enable reproducible ML workflows and efficient feature management.

Distributed Model Training

We optimize training performance through distributed techniques: data parallelism across multiple GPUs, gradient accumulation for large batch sizes, and mixed-precision training for memory efficiency. We combine Horovod, PyTorch DistributedDataParallel, and Databricks clustering to accelerate training while maintaining numerical stability.

Transfer Learning & Model Fine-tuning

We leverage transfer learning to reduce training time and data requirements by starting with ImageNet-pretrained models or domain-specific foundation models. Our approach includes strategic layer freezing, learning rate scheduling, and regularization to adapt pretrained models to specific classification tasks while preventing overfitting.

Hyperparameter Optimization

We use advanced hyperparameter tuning strategies including Bayesian optimization, population-based training, and grid/random search integrated with Databricks. This systematic approach to hyperparameter selection helps us achieve optimal model performance and reduces manual trial-and-error in training workflows.