AI/ML Engineer

Steel Point Solutions is an amazing SBA Certified (8a), HUBZone, Small Disadvantaged Business (SDB) and a Woman Owned Small Business (WOSB) company. Established in 2013 with a vision of offering world class, integrated business solutions for all levels of Government and commercial enterprises. We are represented by a team of talented and qualified professionals who know how essential efficient, cost-effective integrated solutions are to your organization’s success. Leveraging these resources, we strive daily to lead the industry in program management and service delivery.

The AI/ML Engineer is responsible for designing, developing, and deploying machine learning models and artificial intelligence solutions that drive Steel Point’s data-driven initiatives. This role involves creating algorithms, working with large datasets, and implementing AI/ML solutions to solve complex business problems. The AI/ML Engineer will collaborate with data scientists, software engineers, and other stakeholders to integrate and optimize AI/ML systems within existing infrastructure.

• Model Development: Design, develop, and train machine learning models, including supervised, unsupervised, and reinforcement learning algorithms.
• Data Processing: Prepare and preprocess large datasets for training and validation of AI/ML models, including data cleaning, feature engineering, and transformation.
• Algorithm Implementation: Implement and optimize AI/ML algorithms using industry-standard libraries and frameworks (e.g., TensorFlow, PyTorch, Scikit-Learn).
• Deployment: Deploy AI/ML models into production environments, ensuring scalability, reliability, and performance.
• Performance Monitoring: Monitor and evaluate model performance, making necessary adjustments and improvements to enhance accuracy and efficiency.
• Collaboration: Work closely with data scientists, software engineers, and business stakeholders to understand requirements and integrate AI/ML solutions into applications and systems.
• Documentation: Create and maintain documentation for AI/ML models, including design, development, and deployment processes.
• Innovation: Stay updated with the latest advancements in AI/ML technologies and methodologies, applying new techniques to improve existing models and solutions.
• Ethics and Compliance: Ensure AI/ML solutions adhere to ethical guidelines and regulatory requirements, including fairness, transparency, and privacy.

• Bachelor’s degree in Computer Science, Data Science, Engineering, Mathematics, or a related field.
• 3+ years of experience in AI/ML engineering, including hands-on experience with model development and deployment.
• 3+ years of experience with data processing and manipulation using tools such as Pandas, NumPy, and SQL.
• Proven experience with machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, Keras)

• A Master’s degree or PhD in a relevant field is preferred
• Certifications: AI/ML-related certifications (e.g., Google Cloud Certified - Professional Machine Learning Engineer, AWS Certified Machine Learning - Specialty) are preferred. Certifications in data science or analytics (e.g., Certified Data Scientist) are a plus.
• AI/ML-related certifications (e.g., Google Cloud Certified - Professional Machine Learning Engineer, AWS Certified Machine Learning - Specialty) are preferred.
• Certifications in data science or analytics (e.g., Certified Data Scientist) are a plus.

• Machine Learning: Strong knowledge of machine learning algorithms, techniques, and best practices.
• Data Engineering: Proficiency in data processing, feature engineering, and working with large datasets.
• Technical Skills: Experience with AI/ML frameworks and libraries, and programming languages such as Python, R, or Java.
• Problem-Solving: Strong analytical and problem-solving skills to address complex AI/ML challenges and optimize model performance.
• Collaboration: Ability to work effectively with cross-functional teams, including data scientists, engineers, and business stakeholders.
• Communication: Excellent verbal and written communication skills, with the ability to convey complex technical concepts to non-technical audiences.
• Ethical Awareness: Understanding of ethical considerations and regulations related to AI/ML technologies, including fairness and privacy.