Senior ML and AI engineers who build production models, GenAI applications, and agentic systems — not demos. From feature engineering and model training to deployment, monitoring, and LLM-powered products.
★★★★★ 5.0 on Clutch/AWS GenAI Competency/Models running in production
From classical ML pipelines to LLM-powered products and autonomous AI agents — designed for production from the first sprint.
End-to-end model development — feature engineering, training pipelines, hyperparameter tuning, and evaluation frameworks for classification, regression, recommendation, and time-series forecasting problems.
Build LLM-powered products using OpenAI, Anthropic, or open-source models. RAG pipelines, prompt engineering, structured output, multi-turn conversation, and tool-use patterns for real product use cases.
Design and build multi-step AI agents with tool use, memory, planning, and decision loops. Agentic workflows that automate knowledge work reliably enough to put in front of real users.
Deploy models to production using optimised serving infrastructure — Triton, TorchServe, or managed endpoints on AWS, GCP, or Azure. Latency optimisation, batching, quantisation, and cost-per-inference management.
Build the experiment tracking, model registry, feature store, and deployment pipeline your ML team needs to move from notebook to production faster. Built on MLflow, Databricks, or SageMaker depending on your stack.
Most AI demos work once in a notebook. Production AI is a different problem. We engineer systems that behave reliably under real user load and real data drift.
We build systems that handle edge cases, degrade gracefully, monitor for drift, and can be retrained. Demos work once. Production systems work every day.
We don't force LLMs into problems that don't need them. We pick the right approach — classical ML, fine-tuned models, or LLM-based systems — based on your use case and data.
ML is only as good as the features feeding it. Our ML engineers work alongside our data engineering team so the feature pipeline, model training, and serving infrastructure are designed together.
Inference costs and LLM token spend can surprise teams that don't plan for them. We design with cost-per-prediction in mind from the architecture phase.
Real models and AI products — not conference demos or internal prototypes.
Built a production RAG system over 40,000 internal documents — product docs, support tickets, and runbooks. Chunking strategy, embedding pipeline, retrieval tuning, and guardrails for hallucination reduction. Answer accuracy improved from 43% to 91% in blind evaluation vs keyword search baseline.
Trained a gradient boosting model on 3 years of transaction history with 180 engineered features. Deployed to a sub-100ms serving endpoint backed by a Kafka streaming feature pipeline. False positive rate dropped 38% vs the previous rule-based system.
★★★★★The RAG system they built handles queries our previous keyword search couldn't touch. The accuracy improvement was measured, not just claimed.
VP Product, B2B SaaS
★★★★★They designed the feature pipeline and the model together. That end-to-end ownership meant no gaps between what the model expected and what production data looked like.
Head of Risk, Fintech
Flexible models for ML and AI projects at any stage — from an initial proof-of-concept to a production AI platform.
A cross-functional team of ML engineers, AI engineers, and data engineers working exclusively on your AI product or platform. Best for companies building AI as a core product capability.
Embed ML or AI engineers into your existing product or data team. They contribute to sprints from day one and ramp on your codebase, data, and product context quickly. Best for scaling specialist capacity.
Fixed-scope model development, GenAI integration, or agentic system build. We own the delivery end to end and hand over documented, production-ready code and models. Best for defined AI initiatives.
Understand the business problem, available data, latency requirements, and success criteria. Decide whether ML, GenAI, or a rules-based system is actually the right fit.
Establish a measurable evaluation framework and a simple baseline before investing in complex models.
Feature engineering, model training or LLM integration, evaluation runs, and iterative improvement against the baseline.
Production serving infrastructure, monitoring setup, A/B testing framework, and cost measurement.
Model documentation, retraining runbooks, monitoring dashboards, and knowledge transfer to your team.
Whether you're building a GenAI feature, training a production ML model, or standing up an agentic system — we'll help you scope the right architecture before writing a line of code.
Hello, we are BRILLIAN’S. Trying to make an effort to put the right people for you to get the best results. Just insight !
