Role Overview

We are looking for a hands-on Junior AI MLOps Engineer to bridge the gap between model development and production-grade deployment. In this role, you will be responsible for the health of our AI infrastructure, focusing on the deployment, monitoring, and financial optimization of machine learning models. A key part of your responsibility will be evaluating infrastructure choices (e.g., Lambda vs. EC2 vs. Bedrock) to provide leadership with meaningful cost-to-performance metrics that guide our scaling strategy.

Key Responsibilities

• ● Infrastructure Evaluation: Conduct comparative analysis of different AWS hosting environments to determine the most cost-effective deployment strategy for LLMs and specialized ML models.

• ● Cost & Performance Tracking: Develop dashboards and reporting frameworks to monitor "cost-per-inference" and "latency-vs-throughput" metrics across production batches.

• ● Pipeline Automation: Build and maintain CI/CD pipelines for automated model testing, containerization (Docker), and deployment to AWS environments (EKS, ECS, or Lambda).

• ● Resource Optimization: Hands-on management of GPU/CPU utilization to ensure high availability while minimizing cloud spend.

• ● Monitoring & Observability: Implement logging and alerting for model drift, API performance, and infrastructure health to ensure model reliability.

• ● Collaborative MLOps: Work closely with the Data Science team to move models from experimental notebooks to scalable, version-controlled microservices.

Required Skills & Qualifications

• ● Experience: 1–2 years of hands-on experience in MLOps, DevOps, or Backend Engineering with a strong focus on ML infrastructure.

• ● Technical Stack: Proficiency in Python and experience with containerization tools like Docker and orchestration via Kubernetes.

• ● AWS Proficiency: Practical experience with AWS services such as Lambda, EC2, S3, and SageMaker. Knowledge of AWS Bedrock is a plus.

• ● Analytical Mindset: Ability to synthesize complex technical data into clear performance-to-cost reports for stakeholders.

• ● Foundational ML Knowledge: Understanding of the ML lifecycle, including model versioning, feature stores, and API integration (FastAPI/Flask).

• ● Education: Bachelor’s degree in Computer Science, Information Technology, or a related technical field.