MLOps Engineer

Job Overview

We are looking for a skilled MLOps Engineer with 2+ years of experience to join our MLOps & Data Engineering team. The candidate will be responsible for building, deploying, and maintaining machine learning models in production. This role involves working closely with data scientists, software engineers and cloud architects to ensure seamless model deployment, monitoring and scaling.

Key Responsibilities

• Develop and maintain CI/CD pipelines for ML models.
• Deploy, monitor and optimize machine learning models in AWS/Azure/GCP
• environments.
• Automate model training, validation and retraining workflows.
• Implement data versioning, model versioning, and lineage tracking using tools like
• DVC, MLflow, or SageMaker.
• Ensure model performance monitoring, logging, and alerting using Prometheus,
• Grafana, or ELK stack.
• Work with Docker & Kubernetes for containerized deployments.
• Optimize ML model inference pipelines for low latency and cost efficiency.
• Collaborate with Data Engineers to streamline data pipelines for ML workloads.
• Ensure security, compliance, and governance for deployed models.
• Write efficient Python scripts for automating MLOps tasks.
• Perform basic SQL queries for data validation and monitoring.
• Support hyperparameter tuning and model optimization strategies.

Required Skills

• Bachelor’s/Master’s degree in Computer Science, Data Science, or a related field.
• 2+ years of hands-on experience in MLOps, DevOps, or related fields.
• Experience with AWS (S3, Lambda, SageMaker, EKS, Step Functions, Glue,
• CloudWatch, IAM).
• Strong Python programming skills, with experience in TensorFlow, PyTorch or Scikit-
• learn.
• Proficiency in Docker, Kubernetes, Terraform, or CloudFormation.
• Experience with CI/CD tools like GitHub Actions, Jenkins, GitLab CI/CD.
• Familiarity with model monitoring and drift detection frameworks.
• Strong debugging and problem-solving skills for production ML models.
• Basic SQL knowledge for querying and validating ML data.
• Experience in hyperparameter tuning using libraries like Optuna, Hyperopt, or
• GridSearchCV.
• 
  

Good to Have

• Experience with Databricks, Airflow, Kafka, or Spark.
• Exposure to LLMs (Large Language Models) and Generative AI deployments.
• Knowledge of cost optimization strategies for cloud-based ML workloads.

Why Join Us?

• Work on cutting-edge MLOps & Data Engineering projects.
• Exposure to the latest tools and technologies in ML infrastructure.
• Opportunity to grow and contribute to a high-impact fintech ecosystem.
• Be a part of a dynamic and collaborative team led by industry experts.