Machine Learning DevOps Engineer/AI

Location Singapore
Discipline Information & Communications Technology
Job Reference BBBH145266_1731635363
Salary Negotiable
Consultant Name Manalo Frances Diana Delos Santos
Consultant Email [email protected]
Consultant Contact No. 65515326
EA License No. 02C3423
Consultant Registration No. R1219552


Key Responsibilities

  • 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 optimize 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, 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 the integration of AI within the DevSecOps workflow.
    • Create guidelines for evaluating AI model effectiveness, usability, and productivity impact.


Qualifications

  • Education and Experience
    • Bachelor's degree in Computer Science, Engineering, Data Science, or a related field (Master's degree is good to have).
    • 3+ years of experience in MLOps, DevOps, or AI experimentation with a focus on rapid prototyping and MVP development.
    • Good understanding of DevSecOps practices, tools, and methodologies.
  • Technical Skills
    • Proficiency in Python, Golang, Rust or other relevant stack
    • 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 of 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, optimization 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
    • Good 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.


Good to have 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.