Gen AI + ML + MLOps

Role Overview:

The Machine Learning & MLOps Architect is responsible for designing, deploying, and optimizing enterprise-level machine learning (ML) and MLOps architectures. This role involves collaborating with data scientists, engineers, and DevOps teams to build scalable AI/ML solutions, automate model lifecycle management, and ensure robust deployment pipelines for production-grade ML models.

Key Responsibilities:

Machine Learning (ML) Architecture

• End-to-End ML Solution Design: Architect and implement scalable, high-performance ML solutions from data ingestion to model deployment.
• Model Development & Optimization: Guide data scientists in selecting the best ML algorithms, ensuring model explainability, and optimizing computational efficiency.
• ML Infrastructure Scaling: Design infrastructure to handle large-scale data and real-time ML inference using distributed computing frameworks.
• Generative AI & LLMs: Architect solutions for large language models (LLMs), generative AI, and reinforcement learning applications.

MLOps Architecture

• MLOps Pipeline Design: Develop robust CI/CD pipelines for ML models using MLflow, Kubeflow, SageMaker Pipelines, or Vertex AI Pipelines.
• Model Deployment & Monitoring: Implement best practices for ML model versioning, monitoring, drift detection, and automated retraining.
• Cloud & Hybrid ML Solutions: Design and deploy ML workloads on AWS, Azure, GCP, or hybrid on-prem/cloud environments.
• Feature Stores & Data Engineering: Architect feature store solutions (Feast, Tecton) and integrate with data lakes and real-time processing systems.
• Security & Compliance: Implement security best practices for AI models, ensuring compliance with GDPR, HIPAA, or SOC2 standards.

Required Skills & Experience:

• Experience: 8 years in AI/ML, with at least 3 years as an ML or MLOps Architect.
• Programming: Strong expertise in Python, Scala, or Java for ML model development and infrastructure automation.
• ML Frameworks: Proficiency in TensorFlow, PyTorch, XGBoost, or Scikit-learn.
• MLOps & DevOps Tools: Expertise in MLflow, Kubeflow, SageMaker, Azure ML, Vertex AI, Airflow, and Data Version Control (DVC).
• Cloud & Containerization: Hands-on experience with AWS, GCP, Azure, Kubernetes, and Docker.
• Big Data & Streaming: Knowledge of Apache Spark, Kafka, or Snowflake for large-scale ML processing.
• CI/CD & IaC: Experience with Terraform, Jenkins, GitOps, and Helm for infrastructure automation.

Preferred Qualifications:

• Certifications: AWS Certified Machine Learning – Specialty, Google Professional ML Engineer, or Azure AI Engineer.
• LLM & Generative AI: Experience with Hugging Face Transformers, OpenAI API, or fine-tuning LLMs.
• Graph & Explainable AI: Knowledge of graph-based ML, explainability frameworks (SHAP, LIME), and adversarial robustness.