Overview :

We are seeking a skilled Sr. MLOps Engineer to design, build, and maintain robust infrastructure and workflows for scalable machine learning and agentic systems. You will work with modern cloud platforms, Infrastructure as Code, and big data technologies to enable seamless deployment, orchestration, and observability of AI-driven solutions.

Key Responsibilities :

- Design and implement scalable infrastructure for machine learning and agent-based systems using Infrastructure as Code (IaC) tools such as Terraform.

- Build and manage cloud-native solutions leveraging AWS services, including orchestration of multi-agent workflows with platforms like AWS Bedrock.

- Develop and maintain the Model Context Protocol (MCP) Infrastructure to ensure secure, reliable, and scalable communication between agents and backend services.

- Automate data, ML, and agent workflows using GitHub Actions, ensuring robust CI/CD pipelines for model training, deployment, and monitoring.

- Implement and optimize big data processing pipelines using Apache Spark and Databricks to support large-scale data ingestion, transformation, and analytics.

- Implement observability solutions for agentic and ML workflows, including logging, monitoring, distributed tracing, and alerting to ensure system reliability and performance.

- Collaborate with data scientists, engineers, and product teams to operationalize machine learning models and agentic solutions in production environments.

- Ensure compliance with security, data governance, and privacy standards throughout the ML lifecycle.

- Continuously evaluate and adopt best practices in cloud architecture, automation, and MLOps.

Required Qualifications :

- Bachelor's degree in Computer Science, Engineering, or a related field.

- 8+ years of experience in MLOps, DevOps, or backend engineering with a focus on machine learning and/or agentic systems.

- Hands-on experience with cloud platforms (preferably AWS) and Infrastructure as Code tools (e.g., Terraform).

- Proficiency in building and managing CI/CD pipelines using GitHub Actions or similar tools.

- Strong experience with big data platforms such as Apache Spark and Databricks.

- Solid understanding of containerization and orchestration (e.g., Docker, Kubernetes).

- Experience implementing monitoring, logging, and observability for distributed systems.

- Proficiency in Python or another programming language commonly used in ML and automation.

- Familiarity with security best practices for cloud and data systems.

Preferred Qualifications :

- Experience with agent-based architectures and orchestration frameworks (e.g., AWS Bedrock Agents or similar).

- Knowledge of Model Context Protocol (MCP) or similar middleware for secure API communication.

- Experience with Retrieval Augmented Generation (RAG) and embedding models.

- Familiarity with compliance, privacy, and ethical AI practices.

- Strong collaboration and communication skills, with experience working in cross-functional teams