Software test engineer

Summary We are seeking a talented Software Test Engineer to join our engineering team and drive the design, development, and deployment of AI-powered solutions. This role is ideal for professionals passionate about artificial intelligence, machine learning, and software engineering who want to make a tangible impact on real-world applications.

As a Software Test Engineer, you will collaborate with engineers, data scientists, and product managers to build, optimize, and scale AI models and systems. You will work with cutting-edge tools and frameworks while applying your expertise in algorithms, data processing, and system integration to deliver high-performance AI solutions.

Key Responsibilities Design, develop, test, and deploy AI / ML models and applications . Build data pipelines for preprocessing, cleaning, and transforming large datasets.

Train, evaluate, and fine-tune machine learning models for production use.

Integrate AI models into applications and production systems.

Research and implement new AI frameworks, tools, and methodologies to enhance capabilities.

Debug, optimize, and scale algorithms to improve system performance.

Collaborate with cross-functional teams (data science, product, engineering) to deliver end-to-end AI solutions.

Contribute to technical documentation, code reviews, and best practices.

Qualifications Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field . Strong programming skills in Python, Java, or C++ . Solid understanding of machine learning concepts (classification, regression, NLP, computer vision, deep learning). Hands-on experience with frameworks such as Tensor Flow, Py Torch, or scikit-learn . Proficiency in algorithms, data structures, and object-oriented programming.

Experience building and deploying AI applications in real-world environments.

Strong analytical, problem-solving, and debugging skills.

Preferred Qualifications Experience with large-scale data pipelines and distributed systems. Familiarity with cloud platforms (AWS, Azure, or GCP). Knowledge of APIs, microservices, and containerization (Docker, Kubernetes).

Exposure to MLOps practices and continuous integration / continuous deployment (CI / CD). Strong interest in applied AI and innovation in emerging technologies.

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