Job Description:
Deep Learning Engineer – Develop AI Neural Networks
The Deep Learning Engineer is a key contributor to building intelligent systems that power advanced solutions in AI and digital media. In this role, you’ll design, implement, and optimize deep neural networks that address complex, high-impact challenges across a variety of domains.
Your primary responsibility will be to develop state-of-the-art models that drive innovation in areas such as computer vision, natural language processing (NLP), recommendation systems, and generative media. From image recognition to language understanding, your work will form the backbone of AI-powered applications that enhance both internal products and client solutions.
You’ll work closely with data scientists, software engineers, and product teams to move ideas from research into production. Your models won’t stay in notebooks — you’ll deploy them at scale, integrate them into real-world systems, and continuously monitor their performance in dynamic environments.
In this role, you’ll explore and implement deep learning architectures such as CNNs, RNNs, Transformers, and GANs. You’ll fine-tune pre-trained models, train custom networks on domain-specific data, and experiment with new approaches to boost model accuracy, speed, and robustness.
GPU optimization and parallel processing will be part of your day-to-day work. You’ll need strong experience with frameworks like TensorFlow, PyTorch, or JAX, and a solid understanding of performance tuning, batch training, and efficient inference.
You’ll also be involved in building and maintaining deployment pipelines. Whether serving models via APIs, integrating them into mobile or web applications, or scaling inference using containers and orchestration tools like Kubernetes, your technical ownership will ensure reliability and speed.
Cloud infrastructure is critical to your success. You’ll use environments like AWS, GCP, or Azure to train models at scale, manage compute resources, and enable continuous delivery. You’ll also ensure that deployed models meet privacy, security, and compliance standards.
Beyond engineering, communication is key. You’ll explain deep learning concepts clearly, justify model decisions, and collaborate with cross-functional stakeholders to prioritize features, troubleshoot issues, and align model outputs with business value.
The Deep Learning Engineer turns cutting-edge research into real, scalable tools that make products smarter, campaigns more effective, and decision-making more precise.
This role is essential to driving AI innovation throughout the consultancy — helping clients tap into the full power of modern machine learning.
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Responsibilities:
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Design and build deep learning models tailored to digital media challenges.
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Optimize model architectures for performance and scalability.
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Collaborate with data scientists and engineers to integrate models into products.
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Experiment with new algorithms and frameworks.
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Manage GPU resources and model training pipelines.
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Deploy and monitor models in production environments.
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Conduct code reviews and ensure best practices in development.
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Stay updated on the latest research in deep learning.
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Document models, experiments, and results.
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Support knowledge sharing and mentoring within the team.
Preferred Qualifications:
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3+ years experience in deep learning and AI engineering.
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Proficient in Python and deep learning frameworks like TensorFlow or PyTorch.
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Strong understanding of neural networks and architectures.
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Experience with GPU programming and cloud platforms (AWS, GCP).
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Background in computer vision, NLP, or recommendation systems is a plus.
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Bachelor’s or Master’s degree in computer science, AI, or related field.
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Ability to work independently and in collaborative teams.
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Strong analytical and problem-solving skills.
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Familiarity with MLOps and model deployment practices.
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Passion for advancing AI capabilities in digital media.
