AI Solution Architect

AI Solution Architect

Remote

Meckson/Innova Solutions

Job Overview

We are seeking an experienced AI Solution Architect to design, build, and deliver scalable AI/ML solutions that align with business objectives. The ideal candidate will work closely with engineering, product, and data science teams to architect end-to-end AI solutions, ensure system reliability, and guide the organization in adopting modern AI technologies.

Key Responsibilities

• Architect end-to-end AI/ML solutions, including data ingestion pipelines, model development, deployment, and monitoring.
• Collaborate with business stakeholders to translate requirements into scalable technical solutions.
• Evaluate and select appropriate AI/ML frameworks, cloud services, and technology stacks.
• Oversee the integration of AI models into existing products, platforms, and enterprise systems.
• Ensure AI systems follow best practices for performance, security, scalability, and responsible AI guidelines.
• Lead technical design sessions, create architectural diagrams, and define best practices for AI delivery.
• Optimize AI workloads for cost efficiency, latency, model performance, and operational resiliency.
• Mentor engineering and data science teams on architecture patterns, deployment strategies, and MLOps practices.
• Conduct feasibility studies, proof-of-concepts, and prototype development for new AI initiatives.
• Stay current on emerging AI technologies, generative AI models, and cloud-native architectures.

Required Skills & Qualifications

• Bachelor's or Master's in Computer Science, AI/ML, Data Engineering, or related field.
• 7+ years of experience in solution architecture, AI/ML engineering, or cloud architecture.
• Strong expertise in ML frameworks (TensorFlow, PyTorch, Scikit-learn) and Generative AI models.
• Hands-on experience with cloud platforms (AWS, Azure, GCP) and AI/ML services.
• Knowledge of MLOps tools such as Kubeflow, MLflow, SageMaker, Vertex AI, or Azure ML.
• Strong understanding of data engineering, distributed systems, microservices, and API design.
• Experience with deploying models in production using Docker, Kubernetes, or serverless architectures.
• Excellent problem-solving, design thinking, and communication skills.