ML Applications Software Engineer

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Written by AI from the job title and company, not by Harnham, so some details may not match this job. The employer's own page has the real description.

Job Overview

The Machine Learning Applications Software Engineer at Harnham will be responsible for designing, developing, and implementing machine learning models and applications that enhance business outcomes and empower decision-making. This role requires a strong foundation in both software engineering and machine learning principles, enabling the successful candidate to take innovative ideas from conceptualization through to deployment. Collaboration with cross-functional teams to identify problems and provide data-driven solutions will be a core component of this position.

Job Responsibilities

• Design and develop machine learning algorithms and models suitable for business applications.
• Collaborate with data scientists and stakeholders to understand requirements and translate them into robust software solutions.
• Implement and optimize software architectures to support machine learning workflows.
• Conduct performance testing and model evaluations to ensure high reliability and efficiency.
• Maintain documentation for all code and architectures in accordance with best practices.
• Mentor junior developers and contribute to team knowledge sharing.

Qualifications

• Bachelor's or Master’s degree in Computer Science, Engineering, or a related field.
• At least 3 years of experience in software development with a focus on machine learning applications.
• Proficiency in programming languages such as Python or Java.
• Strong understanding of machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn).
• Experience with cloud computing platforms such as AWS, Azure, or Google Cloud.
• Solid grasp of data structures, algorithms, and software design principles.

Technologies & Tools

The position will involve the use of a variety of technologies and tools including programming languages such as Python and Java, machine learning frameworks like TensorFlow and PyTorch, data analysis libraries such as pandas and NumPy, along with cloud services from AWS, Azure, or Google Cloud for deploying models and applications. Version control systems like Git will also be essential for team collaboration.

Ideal Candidates

Ideal candidates will possess a strong analytical mindset, complemented by exceptional problem-solving abilities. They should exhibit a passion for software development and machine learning, along with a commitment to quality and innovation. Excellent communication skills, both verbal and written, are highly desirable as this role requires regular collaboration with team members and stakeholders. Being a proactive learner who stays updated on industry trends will also set candidates apart in this dynamic and evolving field.