MLOps Engineer

Job Description

Lead MLOps Engineer - GenAI

Join our team to lead the development of cutting-edge Generative AI solutions. You'll play a key role in implementing machine learning operations, ensuring seamless deployment, monitoring, and management of our models and data pipelines.

The Team: Work closely with a world-class AI/ML team, including experts in modeling, engineering, data science, and data engineering. Contribute to our AI-driven transformation and drive value for our customers.

Responsibilities:

Lead ML Engineering to build and deploy GenAI services and solutions.

Work on large-scale distributed systems, including infrastructure, data platforms, databases, microservices, and orchestration services.

Develop MLOps/LLMOps platforms and automated pipelines for model deployment, monitoring, and maintenance.

Collaborate with cross-functional teams to integrate ML models into production systems.

Create and manage documentation and best practices for MLOps/LLMOps processes.

Work with technology teams to develop and implement an Enterprise AI platform.

What Were Looking For:

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

8+ years of experience in machine learning, data analytics, or similar roles.

5+ years of experience with:

Writing scalable code in Python (or Scala)

MLOps/LLMOps, machine learning engineering, Big Data

Elasticsearch, SQL, NoSQL, Apache Airflow, Apache Spark, Kafka, Databricks, MLflow

Containerization, Kubernetes, cloud platforms, CI/CD, workflow orchestration

Distributed systems programming, AI/ML solutions architecture, microservices architecture

Preferred Qualifications:

2-3 years of experience with data-driven pipelines for large-scale analytics solutions

Experience with open-source initiatives, research projects, or Kaggle competitions

6-12 months of experience with RAG pipelines, prompt engineering, or Generative AI use cases

Experience with SageMaker and/or Vertex AI

The position is fully remote across the US and will pay up to $250k basic + benefits + 30% bonus for the right individual.

Full-time

Hybrid remote