AI Engineer - Lead

Company Description

At Blend, we are award-winning experts who transform businesses by delivering valuable insights that make a difference. From crafting a data strategy that focuses resources on what will make the biggest difference to your company, to standing up infrastructure, and turning raw data into value through data science and visualization: we do it all. We believe that data that doesn't drive value is lost opportunity, and we are passionate about helping our clients drive better outcome through applied analytics. We are obsessed with delivering world class solutions to our customers through our network of industry leading partners. If this sounds like your kind of challenge, we would love to hear from you. For more information, visit www.blend360.com

Job Description

We are looking for someone who is ready for the next step in their career and is excited by the idea of solving problems and designing best in class. However, they also need to be aware of the practicalities of making a difference in the real world – whilst we love innovative advanced solutions, we also believe that sometimes a simple solution can have the most impact.

Our AI Engineer is someone who feels the most comfortable around solving problems, answering questions and proposing solutions. We place a high value on the ability to communicate and translate complex analytical thinking into non-technical and commercially oriented concepts, and experience working on difficult projects and/or with demanding stakeholders is always appreciated.

What can you expect from the role?

• Contribute to design, develop, deploy and maintain AI solutions
• Use a variety of AI Engineering tools and methods to deliver
• Own parts of projects end-to-end
• Contributing to solutions design and proposal submissions
• Supporting the development of the AI engineering team within Blend
• Maintain in-depth knowledge of the AI ecosystems and trends
• Mentor junior colleagues
  
  

Qualifications

• Contribute to the design, development, testing, deployment, maintenance, and improvement of robust, scalable, and reliable software systems, adhering to best practices.
• Apply Python programming skills for both software development and AI/ML tasks.
• Utilize analytical and problem-solving skills to debug complex software, infrastructure, and AI integration issues.
• Proficiently use version control systems, especially Git and ML/LLMOps model versioning protocols.
• Assist in analysing complex or ambiguous AI problems, breaking them down into manageable tasks, and contributing to conceptual solution design within the rapidly evolving field of generative AI.
• Work effectively within a standard software development lifecycle (e.g., Agile, Scrum).
• Contribute to the design and utilization of scalable systems using cloud services (AWS, Azure, GCP), including compute, storage, and ML/AI services. (Preferred: Azure)
• Participate in designing and building scalable and reliable infrastructure to support AI inference workloads, including implementing APIs, microservices, and orchestration layers.
• Contribute to the design, building, or working with event-driven architectures and relevant technologies (e.g., Kafka, RabbitMQ, cloud event services) for asynchronous processing and system integration.
• Experience with containerization (e.g., Docker) and orchestration tools (e.g., Kubernetes, Airflow, Kubeflow, Databricks Jobs, etc).
• Assist in implementing CI/CD pipelines and optionally using IaC principles/tools for deploying and managing infrastructure and ML/LLM models.
• Contribute to developing and deploying LLM-powered features into production systems, translating experimental outputs into robust services with clear APIs.
• Demonstrate familiarity with transformer model architectures and a practical understanding of LLM specifics like context handling.
• Assist in designing, implementing, and optimising prompt strategies (e.g., chaining, templates, dynamic inputs); practical understanding of output post-processing.
• Experience integrating with third-party LLM providers, managing API usage, rate limits, token efficiency, and applying best practices for versioning, retries, and failover.
• Contribute to coordinating multi-step AI workflows, potentially involving multiple models or services, and optimising for latency and cost (sequential vs. parallel execution).
• Assist in monitoring, evaluating, and optimising AI/LLM solutions for performance (latency, throughput, reliability), accuracy, and cost in production environments.
  
  

Additional Information

• Experience specifically with the Databricks MLOps platform.
• Familiarity with fine-tuning classical LLM models.
• Experience ensuring security and observability for AI services.
• Contribution to relevant open-source projects.
• Familiarity with building agentic GenAI modules or systems.
• Have hands-on experience implementing and automating MLOps/LLMOps practices, including model tracking, versioning, deployment, monitoring (latency, cost, throughput, reliability), logging, and retraining workflows.
• Experience working with MLOps/experiment tracking and operational tools (e.g., MLflow, Weights & Biases).