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Senior AI Solutions Architect
2 semanas atrás
Xcede Recruitment Solutions is delighted to be hiring a Principal Machine Learning Engineer on behalf of a market-leading client.
About the Role
This is an exciting opportunity for an experienced Backend Engineer with a proven track record in building robust software systems who is eager to innovate in the ever-evolving field of AI specifically.
Main Responsibilities
- • Design for Scale: Engineer secure, reliable, and high-performance backend systems and data infrastructures that scale seamlessly.
- • Pioneer Innovation: Lead the AI division to develop and deliver cutting-edge AI models in production environments.
- • Set the Standard: Showcase engineering excellence by driving best practices in coding, reviews, and the software development lifecycle.
- • Inspire and Guide: Mentor senior engineers and tech leaders, helping them grow both technically and professionally.
- • Boost Performance: Spearhead efforts to optimize system performance, streamline data pipelines, and enhance ETL workflows.
- • Integrate with Precision: Collaborate across teams to ensure smooth incorporation of AI solutions into Kaizen's architecture.
- • Stay Ahead of the Curve: Identify and implement emerging technologies to maintain our edge in innovation.
- • Define the Future: Shape the AI technical strategy to align with long-term goals, separate from immediate product priorities.
About You
We are looking for someone with over 10 years of software development experience across various languages and paradigms, with 5+ years in Python. Proven expertise in designing, optimizing, and scaling distributed systems and microservice architectures is essential. Hands-on experience with message brokers like Kafka or RabbitMQ and advanced messaging patterns is also required. Familiarity with DevOps/MLOps practices and containerization tools is desirable. A self-driven approach to tackling complex problems with a focus on performance, security, observability, and quality is crucial. A passion for monitoring and troubleshooting production systems to make data-informed decisions is necessary. Outstanding communication skills to bridge the gap between technical teams and diverse stakeholders are also essential. Bonus points for expertise in classic ML, deep learning, and generative AI algorithms, a deep understanding of the ML lifecycle and its differences from traditional software development, experience building data-intensive, AI-driven products, knowledge of experimentation platforms, feature serving, and model serving, and proficiency with big data technologies like Apache Spark, Delta Lake, Kafka, Flink, and NoSQL databases.
