AI MLOps Engineer

The AI MLOps Engineer will focus on creating and testing innovative AI solutions aimed at improving software development productivity across the entire Software. This role will be responsible for rapid prototyping, developing MVPs, and iterating on product prototypes to integrate AI effectively into our software delivery process. The ideal candidate is experienced in both MLOps and AI experimentation, with a keen focus on delivering solutions that facilitate code generation, quality assurance, testing automation, and continuous delivery.
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.

Qualifications

• Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field
• 2-3 years of experience in MLOps, or AI experimentation with a focus on rapid prototyping and MVP development.

Technical Skills

• Proficiency in Python, Golang, Rust or other relevant .
• 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.