Mid-Level Machine Learning Engineer - Data Engineer II - Chase (London)
New Yesterday
- Build, automate, and maintain ML pipelines for deploying advanced models, including large language models (LLMs), at scale.
- Collaborate with data engineers, scientists and product owners to operationalize workflows for reliable, seamless model deployment and monitoring.
- Implement monitoring, logging, and alerting for AI services, ensuring performance, security, and compliance in production environments.
- Write clean, maintainable, and efficient Python code for ML tooling, orchestration, and infrastructure.
- Develop and maintain infrastructure as code (IaC) using tools such as Terraform or CloudFormation.
- Work with containerization and orchestration technologies (e.g., Docker, Kubernetes) to support scalable and repeatable deployments of AI services.
- Apply robust software engineering best practices-version control, CI/CD, code reviews, testing, and automation-to all aspects of the ML lifecycle.
- Troubleshoot and optimize ML workflows, from initial development through deployment and production support.
- Engage in cross-functional squads, participating in technical discussions, design reviews, and continuous improvement initiatives.
- Contribute to team growth by sharing knowledge and mentoring junior engineers as needed.
- Strong software engineering background, with deep proficiency in Python (and optionally, Go or Java).
- Demonstrated experience deploying and maintaining LLMs (e.g., GPT's, Llama) in production environments.
- Familiarity with frameworks and tooling for LLMs and generative AI (e.g., Transformers, LangChain, Haystack, OpenAI, Vertex AI).
- Experience operationalizing ML solutions in cloud-native environments (AWS, GCP, Azure).
- Proficiency with containerization and orchestration (Docker, Kubernetes or similar) for scalable model deployment.
- Practical experience with infrastructure-as-code (Terraform, CloudFormation, etc.).
- Understanding of concurrency, distributed systems, and scalable API development for ML-powered applications.
- Experience with version control (Git) and CI/CD pipelines.
- Strong problem-solving skills, attention to detail, and a collaborative, growth-focused mindset.
- Experience working in agile, product-driven engineering teams.
- Exposure to Retrieval-Augmented Generation (RAG) pipelines, vector databases (e.g., Pinecone, Weaviate, Milvus), and knowledge bases, with familiarity in integrating them with LLMs.
- Experience with advanced model monitoring, observability, and governance of LLMs and generative AI systems.
- Experience with data engineering or analytics platforms.
- Understanding of AI safety, security, and compliance best practices in production.
- Enthusiasm for learning and adopting the latest MLOps and AI technologies.
- Location:
- London
- Job Type:
- FullTime
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