Job Description & Details
This is a hands-on technical leadership contract for a Lead Machine Learning Engineer who can bridge the gap between heavy research-oriented AI concepts and bulletproof production systems. You'll be stepping into an environment focused on scaling Agentic AI and traditional ML models, meaning you won't just be throwing notebooks over the wall—you'll be architecting the actual pipelines and cloud infrastructure to keep them running reliably.
What You'll Actually Be Doing
Expect to spend your days designing production-grade machine learning applications, setting up robust MLOps workflows, and wrangling distributed data pipelines. You will collaborate closely with product and data science folks to translate ambiguous business requirements into scalable AI architecture. A major chunk of your bandwidth will go toward building out deployment frameworks, optimizing CI/CD pipelines, and establishing model observability so that when things inevitably break in production, you have the metrics to diagnose them fast. Mentoring junior and mid-level engineers on system design best practices is also a daily expectation here.
The Core Tech Stack
You need deep fluency in Python and C++, alongside heavy production experience with frameworks like PyTorch, TensorFlow, and Scikit-learn. Because this is heavily focused on the operational side of AI, your MLOps toolbelt needs to be sharp—expect to use Spark/PySpark for distributed data handling, and GitHub Actions or Jenkins for automated deployments. A strong foundation in SQL, cloud engineering, and REST API design is non-negotiable since you'll be exposing these models as reliable microservices.
Interview Expectations
Be ready for deep-dive system design questions where you'll be asked to architect a real-time Agentic AI pipeline from scratch, dealing with concurrency, latency, and fault tolerance. The hiring manager is going to test your architectural pragmatism, specifically looking for how you handle model drift, monitoring, and rollbacks in production without tanking system availability. They want to see that you understand the trade-offs between distributed compute cost and model inference speed.
Application Advice
To get past the ATS, make sure your resume explicitly highlights your hands-on experience with MLOps frameworks, CI/CD automation, and large-scale cloud deployments. Don't just list the models you've trained; emphasize the infrastructure you've built around them, the size of the data pipelines you've managed with Spark, and how you monitor model performance once it hits production.