Middle Data Engineer AI/ML

Our mission at Geniusee is to help businesses thrive through tech partnership and strengthen the engineering community by sharing knowledge and creating opportunities🌿
Our values are Continuous Growth, Team Synergy, Taking Responsibility, Conscious Openness and Result Driven. We offer a safe, inclusive and productive environment for all team members, and we’re always open to feedback 💜 If you want to work from home or work in the offices in Kyiv or Lviv with stable electricity and Wi-Fi , great — apply right now.

Requirements:

• 2-3+ years of experience in software engineering, with a focus on AI/ML systems or distributed systems;
• Strong coding skills in Python;
• Proven experience in building machine learning models from scratch, including data preprocessing, model architecture design, and training;
• Strong expertise in computer vision tasks, particularly object detection and/or face detection;
• Hands-on experience with model optimization techniques such as quantization, pruning, and knowledge distillation to improve performance on resource-constrained devices;
• Proficiency in Python and deep learning frameworks (e.g., TensorFlow, PyTorch);
• Familiarity with deploying models into production environments and optimizing for inference speed and memory usage;
• Previous work on systems handling millions of users or queries per day;
• Familiarity with cloud infrastructure (AWS, GCP, or Azure) and containerization tools (Docker, Kubernetes);
• Experience with vector search, embedding pipelines, and dense retrieval techniques;
• Proven ability to optimize inference stacks for latency, reliability, and scalability;
• Excellent problem-solving, analytical, and debugging skills;
• Strong sense of ownership, ability to work independently, and a self-starter mindset in fast-paced environments;
• Passion for building impactful technology aligned with our mission;
• Bachelor’s degree in Computer Science or related field, or equivalent practical experience.

Responsibilities:

• Design, build, and scale production-grade inference stacks for AI/ML systems, including computer vision applications;
• Lead the development of efficient inference pipelines and optimized model deployment strategies;
• Drive the end-to-end model lifecycle: data preprocessing, architecture design, training, and production deployment;
• Apply advanced optimization techniques (quantization, pruning, knowledge distillation) to improve inference speed and efficiency on resource-constrained devices;
• Ensure system reliability, observability, and scalability in distributed and real-time environments;
• Collaborate with cross-functional teams to integrate machine learning components into user-facing applications;
• Evaluate and adopt state-of-the-art ML frameworks, tools, and deployment practices to accelerate innovation and improve production readiness;
• Guide architectural decisions, mentor team members, and uphold engineering excellence.