Key Roles & Responsibilities 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. Required Qualifications 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) Candidate Must Have an Active Top Secret SCI Poly Security Clearance. Preferred Qualifications 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.

Skills and Competencies 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. Candidates from Historically Underutilized Business Zones (HUBZone) are strongly encouraged to apply. To determine whether you reside in a HUBZone, visit: https://maps.certify.sba.gov/hubzone/map. The salary range represents a general guideline; however, Steel Point Solutions considers a number of factors when determining base salary offers such as the scope and responsibilities of the position and the candidate's experience, education, skills and current market conditions. 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