AI MLops Engineer

Job Title: AI MLops Engineer

Location: Remote

Job Summary

We are seeking a highly skilled Gen AI Engineer to design, develop, and deploy scalable machine learning and generative AI systems. The ideal candidate will have strong experience in MLOps, cloud platforms, container orchestration, and building end-to-end ML pipelines for enterprise environments.

Key Responsibilities

Design and build data pipelines and engineering infrastructure to support enterprise ML and Gen AI systems.

Productionize machine learning models built by data scientists into scalable, reliable systems.

Develop and deploy tools, APIs, and services for ML training, inference, and Gen AI workloads.

Identify and evaluate new technologies to improve ML system performance, maintainability, and reliability.

Apply software engineering best practices to ML workflows, including CI/CD, automation, testing, and monitoring.

Support model development with versioning, traceability, governance, and data security.

Develop and deploy proof-of-concept AI/ML solutions.

Collaborate with business and technical stakeholders to gather requirements and track delivery.

Required Skills & Qualifications

MLOps & AI/ML

Hands-on experience with Kubeflow, MLflow, Airflow, Argo, or DataRobot.

Experience building MLOps pipelines on AWS, Azure, or Google Cloud Platform.

Strong understanding of ML lifecycle, model deployment, monitoring, and automation.

Exposure to Gen AI, LLMs, vector databases, and embedding-based retrieval (preferred).

Software Engineering & DevOps

Strong programming experience in Python.

Experience with Docker, Kubernetes, OpenShift, and containerized deployments.

Strong knowledge of Linux systems and scripting.

Experience building API integrations across cloud-based systems.

Cloud Platforms & Data

Hands-on experience with AWS, Azure, or Google Cloud Platform ML and computing services.

Experience with cloud databases, data pipelines, and distributed systems.

Experience designing scalable solutions using cloud-native tools.

ML Frameworks

Familiarity with PyTorch, TensorFlow, Keras, Scikit-Learn or similar tools.

Understanding of ML methodologies, evaluation, and experimentation.