Summary
✨ AI‑Generated
Join a senior engineering team building scalable backend systems and machine-learning infrastructure. Develop ML pipelines for training, evaluation and deployment, build data and vector pipelines, implement low-latency model serving and APIs, and collaborate with data scientists and product teams to deliver ML-driven features. The role requires 6+ years of experience.
Highlights
Senior role combining scalable backend engineering with ML infrastructure, production machine-learning systems, low-latency model serving, and close collaboration with data science and product teams.
Description
We're working with a leading digital media company that publishes engaging content across a diverse portfolio of brands, reaching millions of consumers monthly through its innovative platforms.
The Role
Design and build scalable distributed systems and backend platforms compatible with AI/ML infrastructure.
Manage scalable ML pipelines using Vertex AI Pipelines for training, evaluation, and deployment of ranking, retrieval, and recommendation use cases.
Develop and maintain data pipelines for feature generation, model training, and analytics workflows, owning vector generation, storage, and retrieval.
Implement model serving solutions using KServe and build APIs with FastAPI for low-latency inference.
Collaborate with data scientists, product managers, and platform teams to define and deliver ML-driven features and troubleshoot production issues.
Contribute to engineering standards, code quality, and best practices across Python-based services and ML systems.
What You'll Need
6+ years of experience building scalable backend systems and services.
5+ years of experience developing software using object-oriented languages, with strong proficiency in Python, Node.js, and TypeScript.
Hands-on experience with Elasticsearch for search, indexing, and relevance tuning.
Experience with event-driven systems using Apache Kafka for real-time data pipelines and processing.
Familiarity with cloud platforms like AWS and Google Cloud Platform, along with containerization using Docker and orchestration with Kubernetes.
Strong understanding of machine learning concepts, including supervised learning, deep learning, and NLP, with practical application in personalization.
What's On Offer
Opportunity to work on widely used components that help users find ways to consume content on various sites.
Engaging work with technologies such as Vertex AI pipeline, KServe, Kafka, Elasticsearch, and Vector Databases.
Exposure to building capabilities for recommending related articles and leveraging AI/ML power.
A collaborative environment working with product owners, data science, and platform teams.
Apply via Haystack today!
