Job Description

OUR CLIENT

Our client is a successful European company leader in the Maritime industry with a global presence. Currently looking for an LLM DevOps Engineer to bring new ideas to the tech team.

RESPONSIBILITIES

The LLM DevOps Engineer will create and test AI solutions to boost software development productivity. Responsibilities include rapid prototyping developing MVPs and integrating AI into our software delivery process. Ideal candidates have experience in MLOps and AI experimentation focusing on code generation quality assurance testing automation and continuous delivery.

AI Use Case Development and Prototyping

• Develop and rapidly iterate on AI-driven prototypes that support and streamline developer workflows including code specification code generation and testing automation.
• Collaborate with product and DevSecOps teams to identify high-impact AI use cases that improve software development and delivery efficiency.
• Drive proof of concept (PoC) initiatives transforming experimental AI ideas into feasible and scalable solutions
• Implement automation to improve repeatability and reduce manual tasks in the development pipeline such as auto-code generation static code analysis and intelligent error detection.
• Integrate automation tools that improve developer productivity streamline testing and optimise release cycles.
• Stay current with the latest advancements in Al and machine learning technologies.

MVP Development and Iterative Testing

• Build Minimum Viable Products (MVPs) for new AI solutions focusing on quick deployment testing and user feedback.
• Establish efficient testing and evaluation frameworks to assess the effectiveness of AI models and rapidly iterate on improvements.
• Collaborate with developers and QA teams to integrate AI-based prototypes into the broader software lifecycle and measure productivity impact.

End-to-End ML Pipeline Development

• Design and deploy scalable ML pipelines tailored to rapidly evolving prototypes with robust model training testing deployment and monitoring processes.
• Manage versioning model retraining and performance tracking to ensure the continuity of high-quality AI solutions in the production environment.
• Collaborate with cross-functional teams to iterate on solutions based on developer feedback and usage data.
• Establish version control deployment and monitoring standards for ML models across the production environment.
• Develop tools and processes for A/B testing canary releases and other ML model rollout techniques.
• Ensure ML models are efficiently integrated within the internal Software Factory.

Collaboration with DevSecOps Team

• Work closely with DevSecOps engineers to integrate ML workflows with existing CI/CD pipelines.
• Enhance and support security measures for ML processes ensuring compliance with DevSecOps policies and protocols.
• Write scripts and automate workflows to manage ML pipeline processes ensuring faster more reliable and secure model deployments.
• Integrate automation into DevSecOps workflows ensuring repeatability and reducing manual intervention.

Documentation and Compliance

Document AI use cases PoCs MVPs and best practices for integrating AI within the DevSecOps workflow.

Create guidelines for evaluating AI model effectiveness usability and productivity impact.

IDEAL TALENT

Education and Experience

• Bachelors degree in Computer Science Engineering Data Science or a related field (Masters degree preferred).
• 3 years of experience in MLOps DevOps or AI experimentation with a strong focus on rapid prototyping and MVP development.
• Strong understanding of DevSecOps practices tools and methodologies.

Technical Skills

• Proficiency in Python Golang Rust or other relevant languages.
• Experience with ML frameworks (TensorFlow PyTorch LangChain) deployment platforms (Kubernetes) and ML pipeline tools (Kubeflow MLflow).
• Familiarity with CI/CD tools (GitLab CI)
• Knowledge of infrastructure-as-code (IaC) tools like Terraform.
• Understanding data pipelines and tools (e.g. Apache Kafka Spark) for data processing and transformation.
• Experience in developing Restful APIs for Al models.
• Good understanding of machine learning concepts including neural networks optimisation algorithms and evaluation metrics.
• Knowledge of Retrieval Augmented Generation (RAG) techniques.
• Familiarity with prompt engineering techniques like instruction design template-based approaches rule-based conditioning or fine-tuning strategies.

Prototyping and Experimentation Skills

• Demonstrated experience in developing MVPs and iterating on product prototypes with quick turnaround times.
• Skilled in conducting PoCs and building scalable solutions based on experimental results and user feedback.
• Ability to work in a fast-paced agile environment with a focus on continuous experimentation and learning.

Soft Skills

• Strong problem-solving skills and an innovative mindset geared towards improving developer productivity.
• Excellent collaboration and communication skills to work effectively across DevSecOps product and developer teams.
• Self-driven adaptable and capable of managing multiple AI-driven projects in a dynamic setting.

Preferred Skills

• Experience with AI models for code generation automated testing and intelligent debugging.
• Knowledge of security protocols and compliance measures for integrating AI within a DevSecOps environment.
• Experience with Agile methodologies.
• Familiarity with monitoring and observability tools (e.g. Prometheus Grafana) and model explainability tools.

If you have the skills and experience we are looking for please submit your resume and cover letter to us. We look forward to hearing from you!

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