Job Description: MLOps Engineer

Why you’ll love this role

StockX is an established global startup headquartered in the USA with development offices in Bangalore India.

We are seeking a versatile and skilled MLOps Engineer with expertise in DevOps, CloudOps (preferably AWS Cloud), and foundational knowledge of Data Engineering and Software Engineering. The ideal candidate should have 4-5 years of relevant experience, a solid understanding of deploying, managing, and scaling machine learning pipelines in production, and experience in backend software development and API design.

What you’ll do

• Design, build, and maintain end-to-end machine learning pipelines, including model training, validation, deployment, monitoring, and updating.
• Implement CI/CD pipelines tailored for machine learning workflows.
• Enhance ML Pipeline Efficiency Directly improve the robustness scalability and performance of our TensorFlow and Kubeflow pipelines
• Focus on optimizing model training and serving processes minimizing downtime and automating routine tasks to increase operational efficiency
• Drive Platform Scaling & Innovation Take a leadership role in expanding our ML platform's capacity to manage larger data volumes and more complex models
• Research and integrate cutting-edge technologies develop scalable architectures and elevate system performance and efficiency through continuous enhancements
• Establish MLOps Excellence Design and implement robust MLOps frameworks that streamline the integration continuous deployment and monitoring of ML models
• Set up comprehensive CI/CD pipelines automate testing and create monitoring tools to proactively track model performance and detect issues
• Foster Cross-Functional Collaboration Partner with data scientists software engineers and product teams to transform business requirements into scalable and dependable machine learning solutions
• Bridge the gap between model development and deployment ensuring models are production-ready and align with performance standards
• Overcome Production Challenges Proactively monitor troubleshoot and resolve issues affecting model performance data pipeline integrity and system efficiency
• Identify root causes and implement strategic solutions to ensure the ongoing stability and performance of our ML infrastructure
• Streamline cloud infrastructure (AWS preferred), automate deployments with IaC tools, and ensure scalable, reliable, and high-performing ML workflows.
• Deploy ML models with containerization and orchestration, and establish monitoring systems for performance, data drift, and system health.
• Develop scalable backend solutions, integrate ML models via APIs, ensure clean code practices, with optional frontend expertise.
• Collaborate across teams to align ML solutions with business goals and document processes for reproducibility and excellence.
• Support data engineering by integrating pipelines, optimizing storage, and preprocessing datasets for ML workloads.

About you

• Educational and Technical Foundation Bachelor's degree in Computer Science or a related technical field or equivalent practical experience.
• You should have solid experience in maintaining and scaling machine learning pipelines using TensorFlow and Kubeflow.
• Knowledge of ML Engineering, including model architecture design and hyperparameter tuning.
• Advanced MLOps Proficiency At least 3 years of experience in ML Engineering with expertise in deploying models and managing ML workflows and familiarity with MLflow TFX or Airflow.
• Strategic Problem Solver with Collaborative Spirit Excel at solving complex problems at scale and have a proven ability to work effectively within collaborative fast-paced cross-functional teams.
• You're adept at communicating technical concepts across various stakeholder groups ensuring alignment and understanding.
• Innovative Tech Enthusiast with Cloud Expertise Your proactive approach drives you to continually seek improvements in ML development and deployment processes.
• You have a strong knowledge of cloud platforms particularly AWS and experience with containerization tools like Docker and Kubernetes. Hands-on experience with AWS Cloud (EC2, S3, RDS, Lambda, SageMaker, etc.).
• Strong programming skills in Python, with experience in frameworks like TensorFlow, PyTorch, scikit-learn, LLM / Lang chain / Agent experience is a plus.

Nice to have skills

• Understanding of embeddings / vector databases and feature stores
• Knowledge of monitoring tools like Prometheus, Grafana, or CloudWatch.
• Experience with other cloud platforms (GCP, Azure) is a plus.
• Understanding of model explainability, fairness, and ethical considerations in AI/ML.
• Experience with version control tools like Git and ML model versioning tools like MLflow or DVC.