Machine Learning DevOps Engineer (Central) (PRIT)

Job Summary

We are looking for an experienced MLDevOps Engineer to develop and prototype innovative AI solutions aimed at enhancing software development productivity. This role will focus on creating rapid prototypes, developing MVPs, and integrating AI into the software delivery pipeline, with a specific emphasis on code generation, quality assurance, test automation, and continuous delivery. The ideal candidate will have expertise in both MLOps and AI experimentation, with a focus on improving developer workflows and overall software development efficiency.

Key Responsibilities

AI Use Case Development and Prototyping

• Rapidly design, prototype, and iterate AI-driven solutions that support developer workflows such as code specification, generation, and test automation.
• Collaborate with product and DevSecOps teams to identify and prioritize AI use cases that enhance software development efficiency.
• Lead proof-of-concept (PoC) initiatives, transforming experimental AI ideas into scalable solutions.
• Implement automation to streamline the development pipeline, including auto-code generation, static code analysis, and error detection.
• Integrate automation tools to boost developer productivity, streamline testing, and optimize release cycles.
• Stay up to date with the latest trends and advancements in AI and machine learning technologies.

MVP Development and Iterative Testing

• Build Minimum Viable Products (MVPs) for new AI solutions, focusing on fast deployment, testing, and user feedback.
• Establish efficient testing frameworks to evaluate AI models and iterate on improvements quickly.
• Work closely with developers and QA teams to integrate AI-based prototypes into the software lifecycle and assess their impact on productivity.

End-to-End ML Pipeline Development

• Design and deploy scalable ML pipelines that support rapid prototyping, with strong processes for model training, testing, deployment, and monitoring.
• Manage versioning, retraining, and performance tracking to maintain high-quality AI solutions in production.
• Collaborate with cross-functional teams to refine solutions based on developer feedback and usage data.
• Establish standards for version control, deployment, and monitoring of ML models in production environments.
• Develop tools and processes for A/B testing, canary releases, and other ML rollout techniques.
• Ensure smooth integration of ML models within the internal development ecosystem.

Collaboration with DevSecOps Team

• Collaborate with DevSecOps engineers to integrate ML workflows into existing CI/CD pipelines.
• Support security measures for ML processes to ensure compliance with DevSecOps policies.
• Automate workflows and scripts to manage ML pipeline processes for faster, more reliable, and secure deployments.
• Integrate automation into DevSecOps workflows, ensuring repeatability and reducing manual tasks.

Documentation and Compliance

• Document AI use cases, PoCs, MVPs, and best practices for integrating AI into DevSecOps workflows.
• Create guidelines for evaluating AI models’ effectiveness, usability, and productivity impact.

Qualifications

Education and Experience

• Bachelor’s degree in computer science, Engineering, Data Science, or related field (Master’s degree preferred).
• 3+ years of experience in MLOps, DevOps, or AI experimentation, with a focus on rapid prototyping and MVP development.
• Strong understanding of DevSecOps practices and methodologies.

Technical Skills

• Proficiency in Python, Golang, Rust, or other relevant languages.
• Experience with ML frameworks (e.g., TensorFlow, PyTorch, LangChain), deployment platforms (e.g., Kubernetes), and ML pipeline tools (e.g., Kubeflow, MLflow).
• Familiarity with CI/CD tools (e.g., GitLab CI).
• Knowledge of Infrastructure-as-Code (IaC) tools like Terraform.
• Familiarity with data processing tools like Apache Kafka, Spark.
• Experience in developing RESTful APIs for AI models.
• Strong understanding of machine learning concepts, including neural networks, optimization algorithms, and evaluation metrics.
• Familiarity with Retrieval Augmented Generation (RAG) and prompt engineering techniques (e.g., instruction design, template-based approaches).

Prototyping and Experimentation Skills

• Proven experience in developing MVPs and iterating on prototypes with quick turnaround times.
• Experience in conducting PoCs and scaling solutions based on experimental results and user feedback.
• Ability to thrive in a fast-paced, agile environment, focusing on continuous experimentation and learning.

Soft Skills

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

Interested candidates who wish to apply for the advertised position, please click on “Apply”. We regret that only shortlisted candidates will be notified.