Machine Learning Engineer

We are seeking a Machine Learning Operations (ML Ops) Engineer to design, build, and maintain scalable infrastructure and pipelines that enable the deployment, monitoring, and management of AI and machine learning models across the enterprise.

This role sits at the intersection of data engineering, Data Ops, and data science ensuring that models move seamlessly from experimentation to production and deliver measurable business value. The ideal candidate combines strong engineering fundamentals with a deep understanding of modern ML tools, cloud technologies, and automation frameworks.

• Design, develop, and manage robust ML pipelines for training, testing, deployment, and monitoring.
• Utilize prompt engineering for crafting and refining effective prompts to guide LLMs.
• Adapt pre-trained LLMs to specific tasks and datasets.
• Improve the speed and efficiency of LLMs.
• Understand the underlying architecture of LLMs, including self-attention mechanisms.
• Collaborate with data scientists and engineers to streamline the model lifecycle — from experimentation to production.
• Implement CI/CD pipelines for ML workloads
• Manage and optimize model storage, versioning, and governance
• Monitor production models for performance drift, accuracy, and reliability, implementing automated retraining and alerting.
• Build and maintain containerized environments for scalable ML workloads.
• Ensure compliance with data privacy, security, and governance policies throughout the ML lifecycle.
• Optimize compute and storage costs across AWS, Azure, or GCP environments.
• Collaborate with architecture and infrastructure teams to define best practices for model deployment and performance optimization.
• Support data scientists with reproducibility, experimentation tracking, and environment consistency.
• Incorporate human feedback loops to monitor and improve LLM performance and safety.
• Build, Document, Execute, and Govern ML Ops Processes

• Proficiency in Python and familiarity with ML frameworks.
• Strong experience with CI/CD pipelines, infrastructure as code, and container orchestration.
• Hands-on experience with cloud ML services.
• Strong experience with LLMs.
• Experience with AI/ML & Datalakehouse Platforms (Databricks)
• Understanding of model lifecycle management, including versioning, monitoring, and retraining.
• Familiarity with API development and integration for serving ML models (FastAPI, Flask, or similar).
• Strong understanding of data pipelines, ETL processes, and distributed data systems.
• Experience with MLOps orchestration tools.
• Knowledge of security and compliance in AI/ML systems.
• Background in software development best practices, including code review, testing, and documentation.

Eight Eleven Group provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, national origin, age, sex, citizenship, disability, genetic information, gender, sexual orientation, gender identity, marital status, amnesty or status as a covered veteran in accordance with applicable federal, state, and local laws