ML Engineer

Role Overview

We are looking for a Machine Learning Engineer to design, develop, and deploy scalable ML solutions in a cloud-based environment. In this role, you will work at the intersection of machine learning, data engineering, and knowledge graph technologies, helping build intelligent systems that process and analyze complex data.

You will collaborate with data scientists, engineers, and domain experts to develop machine learning models, implement data pipelines, and deploy production-ready ML systems using modern MLOps practices and cloud technologies.

Key Responsibilities

• Design, develop, and deploy machine learning models and AI-driven solutions.

• Build and maintain data pipelines and ETL/ELT processes for ML workflows.

• Implement MLOps practices, including CI/CD for machine learning pipelines and model deployment.

• Work with NLP techniques, including Named Entity Recognition (NER) and Retrieval-Augmented Generation (RAG).

• Develop and integrate knowledge graph–based solutions using RDF/SPARQL.

• Work with vector databases to support semantic search and ML-driven applications.

• Deploy and manage ML workloads in Google Cloud Platform (GCP) using services such as Vertex AI, Dataflow, and GKE.

• Utilize distributed computing frameworks such as Ray and HPC environments for large-scale model training.

• Optimize model performance using GPU acceleration (CUDA).

• Collaborate with cross-functional teams in an Agile development environment.

Required Skills

• Strong experience with Python for machine learning and data processing.

• Experience building and deploying machine learning models in production.

• Knowledge of MLOps practices, including CI/CD for ML systems.

• Experience with NLP techniques, including NER and RAG.

• Understanding of Knowledge Graphs and semantic technologies (RDF/SPARQL).

• Experience with SQL and data engineering concepts (ETL/ELT).

• Experience with Google Cloud Platform, particularly:

  • Vertex AI

  • Dataflow

  • GKE

• Experience with distributed computing frameworks such as Ray.

• Familiarity with vector databases and semantic search architectures.

• Knowledge of GPU computing and CUDA for ML acceleration.

• Strong communication skills and English proficiency at C1 level.

• Experience working in Agile development environments.