Do you provide Data Engineers and ML Engineers in the same pod?

Yes. Most AI initiatives fail because data pipelines and ML deployments operate in silos. Our pods are cross-functional by design, combining data and ML engineering to guarantee end-to-end pipeline velocity.

What seniority level are your data engineers?

Senior and Principal talent exclusively. Our hands-on regional academy and rigorous technical evaluation process filter out theoretical knowledge, validating only engineers with proven, production-grade enterprise experience on high-scale systems.

Can your team work in our existing Databricks workspace?

Yes. We conduct an upfront audit of your native architecture including Unity Catalog governance schemas, cluster policies, and active job configurations integrating directly without forcing unnecessary rearchitecting.

How do you handle model evaluation?

With automated CI/CD quality gates. We build evaluation suites before pipeline construction, enforcing strict automated benchmarks to continuously test accuracy, hallucination drift, and execution latency.

Do you work with open-source models or proprietary APIs?

We remain completely model-agnostic. We architect solutions using open-weight models (like Llama or Mistral) to optimize compute costs and data privacy, or commercial frontier APIs (like OpenAI or Anthropic) when maximum reasoning power is required.

AI & Data Engineering

Enterprise AI infrastructure and deployment pipelines. Agilyti delivers dedicated AI, Databricks, MLOps, and full-stack pods; guided by US leadership with Balkans technical depth in Kosovo.

What an Agilyti AI & Data Team Ships

Production-grade data architectures, automated feature pipelines, and custom model deployments. Engineered directly into your repository.

Data Lakehouse Architecture

Medallion architecture mapping (Bronze → Silver → Gold) utilizing Databricks, Delta Lake, and Snowflake. Complete Unity Catalog data governance ensuring production pipelines your internal team can explicitly trust.

Production Feature Engineering

Production-ready feature stores engineered for deterministic reuse across enterprise models. Execution of point-in-time correct joins to eliminate feature leakage, leveraging MLflow Feature Store or Feast frameworks tailored to your native stack.

Production RAG Pipelines

Optimized retrieval-augmented generation covering secure document ingestion, strategic chunking, multi-modal embedding, vector database retrieval, and reranking. Latency-budgeted architectures with integrated evaluation suites prior to deployment.

Model Fine-Tuning & Custom SFT

Execution of LoRA and QLoRA parameters on open-weight foundation models. Comprehensive supervised fine-tuning (SFT) on isolated proprietary datasets, validated through rigorous evaluation suites before and after every training run.

Batch & Real-Time Streaming Pipelines

Airflow-orchestrated batch data processing alongside Apache Spark structured streaming for sub-minute data latency. Distributed Kafka event-driven architectures engineered for real-time operational analytics.

Data Quality & CI/CD Observability

Automated Great Expectations and dbt testing frameworks integrated directly into your repository CI/CD pipelines. Continuous column-level anomaly detection and real-time operational alerts to capture issues before stakeholder impact.

AI products fail at the pipeline and we fix all of it.

AI & Data Delivery
Models

Embedded Data Engineering Pods

Senior data engineering teams that integrate into your repository to build, refactor, and maintain high-throughput pipelines across Databricks, Snowflake, and dbt.

Applied AI & ML Engineering Pods

Specialized engineering units designed to operationalize AI. We build RAG pipelines, fine-tune models, and deploy production MLOps without exposing private data.

Specialized Balkans Engineering Depth

Direct access to top-tier AI and data talent out of Pristina, Tirana, and Skopje, backed by US accountability and a proven track record of sub-6% attrition.

Contact Us

AI & DataEngineersDeployed

Integrated pipeline engineering and machine learning deployment across global delivery tracks.

250+

AnnualAttritionAcross Pods

High-retention engineering operations running significantly below industry staffing averages.

<6

RapidOnboarding &Kickoff

Streamlined team configuration, technical lead integration, and automated toolchain onboarding to get your pod sprint-ready quickly.

6

Scale Faster with Enterprise-Grade AI Infrastructure.

Deploy production-ready data pipelines, agentic workflows, and secure MLOps in days not quarters. Explore our full suite of core capabilities engineered for enterprise scale and zero operational friction.