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AI Innovation Leader
1 semana atrás
**About the Role**
Xcede is delighted to be hiring for a Principal ML Engineer on behalf of a market-leading client. In this role, you'll be at the forefront of designing and deploying advanced, scalable, and high-performing data & AI solutions.
We're seeking 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.
Key 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.
You'll Bring
- 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.
- Hands-on experience with message brokers like Kafka or RabbitMQ and advanced messaging patterns.
- Familiarity with DevOps/MLOps practices and containerization tools.
- A self-driven approach to tackling complex problems with a focus on performance, security, observability, and quality.
- A passion for monitoring and troubleshooting production systems to make data-informed decisions.
- Outstanding communication skills to bridge the gap between technical teams and diverse stakeholders.
Bonus Points
- 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.
- Proficiency with big data technologies like Apache Spark, Delta Lake, Kafka, Flink, and NoSQL databases.
