Technologies: Python, R, SQL, AWS, Azure, (managed) kubernetes, serverless, Docker, Terraform, CI/CD (Azure Actions, Github Actions, Bitbucket, GitLab), ElasticSearch, MLOps, LLMOps, PyTorch, Scikit-learn, Dagster, Prefect, Flask, FastAPI, the modern LLM stack (?) incl. Langchain/Llama-index/haystack/instructor/... (although I don't recommend the first 2), modern data stack (duckdb, airbyte, dbt, great expectations, various managed cloud services, ...)
Résumé/CV: DM me
Email: aiwork.unified197@slmail.me (alias, feel free to DM)
AI/ML engineer/architect with 6 YOE building and scaling production-grade AI, cloud-native systems. Expertise in full AI lifecycle, MLOps, and leading multidisciplinary teams in developing greenfield AI products.
Notable achievements include:
* Deploying LLM-powered products and RAG systems for enterprises with millions of users driven by transformation of business process, investment into and revision of information architecture, and use of proper mature tooling, rather than fancy agentic workflows that just don't work today
* Frequently ramped up quickly in new business domains to find pragmatic simple algorithms rather than complex, expensive AI solutions, working with C-suite, domain experts & engineers to drive value, e.g. 20x actionable leads for sales teams by using public data
* Productised AI-powered solutions with reliable XXMEUR impact across AI domains (computer vision, vision understanding, nlp, llms, time series, classical statistical modeling, audio generation & transcription, ...) leading multidisciplinary teams with minimal budgets
* Helped establish a neuroscience center of excellence, significantly impacted AI & corporate strategy at large enterprises, influencing international policy & strategic partnerships
I'm looking for opportunities to go full founder mode and build AI-powered systems to do good and can bring many different angles to the table.
Remote: Yes
Willing to relocate: Yes
Technologies: Python, R, SQL, AWS, Azure, (managed) kubernetes, serverless, Docker, Terraform, CI/CD (Azure Actions, Github Actions, Bitbucket, GitLab), ElasticSearch, MLOps, LLMOps, PyTorch, Scikit-learn, Dagster, Prefect, Flask, FastAPI, the modern LLM stack (?) incl. Langchain/Llama-index/haystack/instructor/... (although I don't recommend the first 2), modern data stack (duckdb, airbyte, dbt, great expectations, various managed cloud services, ...)
Résumé/CV: DM me
Email: aiwork.unified197@slmail.me (alias, feel free to DM)
AI/ML engineer/architect with 6 YOE building and scaling production-grade AI, cloud-native systems. Expertise in full AI lifecycle, MLOps, and leading multidisciplinary teams in developing greenfield AI products.
Notable achievements include:
* Deploying LLM-powered products and RAG systems for enterprises with millions of users driven by transformation of business process, investment into and revision of information architecture, and use of proper mature tooling, rather than fancy agentic workflows that just don't work today
* Frequently ramped up quickly in new business domains to find pragmatic simple algorithms rather than complex, expensive AI solutions, working with C-suite, domain experts & engineers to drive value, e.g. 20x actionable leads for sales teams by using public data
* Productised AI-powered solutions with reliable XXMEUR impact across AI domains (computer vision, vision understanding, nlp, llms, time series, classical statistical modeling, audio generation & transcription, ...) leading multidisciplinary teams with minimal budgets
* Helped establish a neuroscience center of excellence, significantly impacted AI & corporate strategy at large enterprises, influencing international policy & strategic partnerships
I'm looking for opportunities to go full founder mode and build AI-powered systems to do good and can bring many different angles to the table.