Specialist, AI Engineering

Role Overview

We are seeking a technically strong and dependable AI Engineer to maintain, optimize, and evolve our production AI ecosystem — focusing on computer vision and applied AI use cases that scale across domains.

This role ensures the continuous health, retraining, and reliability of deployed AI models and pipelines. You will work closely with AI Ops, Solution Architects, and Data Engineering to sustain a production environment that is robust, traceable, and future-ready.

Key Responsibilities

• Monitor model performance in production to detect accuracy drift, bias, or system degradation.
• Manage model retraining cycles, including data updates, validation, and redeployment through automated CI/CD pipelines.
• Maintain and enhance existing data pipelines for image, video, and multimodal inputs to ensure stable ingestion and consistent performance.
• Perform controlled improvements and fine-tuning of production models using TensorFlow or PyTorch.
• Automate evaluation and reporting, building tools and dashboards for visibility into system health and retraining outcomes.
• Collaborate with AI Ops and Data Engineering to ensure versioning, reproducibility, and alignment with governance standards.
• Support small-scale enhancements or greyscale, ensuring they meet production-readiness and compliance requirements before rollout.
• Contribute to model governance — ensuring all deployed models remain explainable, auditable, and compliant with internal AI policies.

Required Skills & Experience

Soft Skills

• Strong communicator and collaborator across engineering and ops teams.
• Reliable and detail-oriented; prioritizes system stability and maintainability.
• Ownership mindset with focus on long-term operational excellence.

Technical Skills

• Proficient in Python, with hands-on experience in TensorFlow and PyTorch for retraining and fine-tuning.
• Strong understanding of data pipelines (batch or stream) for unstructured and structured data.
• Familiar with CI/CD workflows and model lifecycle management in production.
• SQL proficiency for validation, monitoring, and data quality assurance.
• Knowledge of model monitoring, version control (Git), and configuration management.

Preferred Qualifications

• Experience with MLOps tools (MLflow, Weights & Biases, Kubeflow).
• Understanding of AI deployment infrastructure, including:
  • RESTful or MCP-based APIs for inference and context exchange
  • Scalable storage and retrieval for image and multimodal data
  • Secure and compliant deployment under AI governance standards
• Exposure to model optimization and conversion (ONNX, TensorRT, TFLite).
• Proven track record maintaining production-grade AI models and implementing retraining automation.