ML Systems Engineer — Production-Ready AI Apps

ActAI; Greater London; In loco; GBP 90.000 - 140.000; Tempo pieno

Descrizione del lavoro

ActAI is hiring ML engineers to build and improve AI-native applications. You will own end-to-end ML solutions from model development to production deployment, focusing on classification, ranking, retrieval and personalization.

You will work on production-ready pipelines, from data handling and model evaluation to deploying scalable inference systems, collaborating with engineers and product teams to ship reliable ML-powered features.

Competenze

• Solid ML foundations and modern neural architectures.
• Experience deploying ML models to production systems.
• Background in classification, ranking, retrieval or personalization.
• Familiar with model evaluation, experimentation and metrics.
• Writes production-quality code with real-world data.
• Balances model quality, latency, cost and reliability.
• Strong ownership and problem-solving skills.

Mansioni

• Build and improve ML models for classification, ranking and personalization.
• Develop efficient classifiers and optimize accuracy, latency and cost.
• Improve search relevance and context retrieval using embeddings and reranking.
• Build personalization models that respect user privacy.
• Develop evaluation pipelines, datasets and metrics to measure quality.
• Fine-tune and adapt models with sufficient data.
• Build and maintain ML training, inference and deployment pipelines.

Conoscenze

ML foundations

Production ML

Classification/Ranking

Model evaluation

Production code

Latency optimization

Ownership

Strumenti

Python

PyTorch/JAX

Embeddings/Vector search

Production ML pipelines

Descrizione del lavoro

ActAI is hiring ML engineers to build and improve AI-native applications. You will own end-to-end ML solutions from model development to production deployment, focusing on classification, ranking, retrieval and personalization.

You will work on production-ready pipelines, from data handling and model evaluation to deploying scalable inference systems, collaborating with engineers and product teams to ship reliable ML-powered features.