What kind of ML engineering work does Mark Asare do?

Mark Asare builds production machine learning systems including model training pipelines, evaluation workflows, deployment, monitoring, and the surrounding cloud infrastructure needed to run them reliably.

Does Mark Asare work across both AWS and Azure?

Yes. Mark Asare works across AWS and Azure, with hands-on delivery involving Azure ML, Azure OpenAI, AKS, Synapse, AWS Lambda, DynamoDB, S3, CloudFront, SES, and Route 53.

Can Mark Asare build full-stack products as well as ML systems?

Yes. In addition to ML and AI systems, Mark Asare builds end-to-end products with backend services, APIs, deployment infrastructure, operational workflows, and public-facing interfaces.

Capabilities

ML Engineer, AI Engineer, Full-Stack Engineer, AWS & Azure Cloud Engineer.

I build machine learning systems, AI applications, cloud platforms, and full-stack products that are designed to run in production, not just in notebooks or slide decks.

Hire Me for a Project View Proof of Work

What I Own

End-to-end engineering across models, product, and operations.

This is the layer where most search pages get vague. Mine should not. I work across the parts that actually determine whether a system becomes useful: model quality, product shape, cloud infrastructure, deployment, and operational reliability.

Machine Learning Engineering

Model training, evaluation, forecasting, anomaly detection, ranking, experimentation, monitoring, and deployment pipelines for production systems.

CatBoostPyTorchTensorFlowscikit-learnTime SeriesMonitoring

AI Engineering

RAG systems, Azure OpenAI workflows, prompt orchestration, evaluation loops, and practical AI features that fit inside real business operations.

Azure OpenAIAzure AI FoundryLangChainLangSmithRAGLLM Ops

Full-Stack and Cloud Delivery

Backend services, APIs, public-facing interfaces, deployment workflows, payments, messaging, and the operational glue that makes software stay live.

FlaskDjangoAWS LambdaAKSCI/CDPlatform Ops

Cloud and Ops

AWS, Azure, and the operational layer around them.

I am not only interested in models. I work on the systems that surround them: compute environments, APIs, storage, pipelines, observability, and deployment processes.

Azure Delivery

Azure MLAzure OpenAIAzure AI FoundryAKSSynapseAzure DevOps

AWS Delivery

LambdaDynamoDBS3CloudFrontSESRoute 53

Operations

CI/CDTerraformGitHub ActionsDockerKubernetesMonitoring

Product Engineering

APIsAdmin PanelsPaymentsAuthFrontend UIBackends

Proof

What that looks like in practice.

This page is not the full work history. It is the compact capability view: the kinds of environments and delivery problems I tend to own.

Commerce and Forecasting

Production ML for conversion, planning, forecasting, and operational decision support in environments where accuracy and timing matter.

Enterprise AI Reporting

RAG and LLM workflows that turn internal data into usable reports, summaries, and decision-support tooling instead of demo-only chat surfaces.

Cloud and Delivery Ownership

Infrastructure, CI/CD, deployment, APIs, and reliability work across Azure and AWS so the system keeps running after launch.

FAQ

Common hiring and search questions.

Can you build both ML systems and software products?

Yes. I work across modelling, APIs, infrastructure, deployment, and user-facing product delivery, which is why my portfolio includes both ML systems and live web platforms.

Do you work across AWS and Azure?

Yes. My work includes Azure ML, Azure OpenAI, AKS, Synapse, and Azure DevOps as well as AWS Lambda, DynamoDB, S3, CloudFront, SES, and Route 53.

Do you handle operations and deployment too?

Yes. I care about what happens after the model or product is built: CI/CD, cloud setup, release workflows, reliability, and the operational edges that determine whether a system keeps working.