Machine Learning Engineer

Company Description D2N Solutions is a talent and solutions provider focused on helping organizations build high-performing teams that align with their culture and business goals. The company’s multidisciplinary team brings expertise in recruitment, compliance, finance, and marketing, offering comprehensive support tailored to the needs of both clients and candidates. D2N Solutions delivers a broad range of information technology services, including artificial intelligence, big data, cloud computing, enterprise applications, and security. The organization also provides technology infrastructure services such as data centers, networks, and IT help desk, as well as business consulting across industries like banking, healthcare, retail, telecom, and education. This combination of domain knowledge and staffing experience creates opportunities for technical professionals to work on diverse, industry-specific projects.

Role Description The Machine Learning Engineer will design, develop, and deploy machine learning models to support data-driven solutions for clients across various industries. In this full-time remote role, the engineer will collaborate with data scientists, software engineers, and business stakeholders to understand requirements, select appropriate algorithms, and implement scalable ML pipelines. Daily responsibilities include data preprocessing, feature engineering, model training and evaluation, and performance optimization in production environments. The role also involves integrating models into existing systems, monitoring their performance, and iterating based on feedback and new data. The engineer will contribute to documentation, code reviews, and best practices to ensure reliability, security, and maintainability of ML solutions.

Qualifications

• Strong foundation in Computer Science, including data structures and Algorithms for building efficient and scalable ML solutions.
• Applied experience with Statistics and Pattern Recognition for data analysis, feature selection, and model evaluation.
• Hands-on expertise with Neural Networks and modern machine learning frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related quantitative field.
• Proficiency in programming languages commonly used in ML (e.g., Python, R, or Java) and experience with version control tools (e.g., Git).
• Experience building end-to-end ML pipelines, including data ingestion, model deployment, and monitoring in cloud or on-prem environments.
• Ability to work independently in a remote setting, manage multiple priorities, and communicate technical concepts clearly to non-technical stakeholders.
• Familiarity with big data technologies (e.g., Spark, Hadoop), MLOps practices, and security considerations for ML systems is a plus.