Software Engineer - Machine Learning

Summary

Join HIKE2, a leading technology advisory and implementation services provider, as a Software Engineer - Machine Learning Consultant. You will be responsible for developing, implementing, and maintaining machine learning models and systems, bridging the gap between data science research and production-ready software. This role demands a strong foundation in software engineering and machine learning principles. You will build and deploy ML solutions, integrate ML with applications, shape ML architecture, collaborate cross-functionally, and manage the model lifecycle. Data preparation and feature engineering are also key responsibilities. The position offers a competitive salary and a comprehensive benefits package.

Requirements

• Bachelor’s Degree in Computer Science, Engineering, Mathematics, or a related quantitative field
• 3–5 years of experience in developing and deploying statistical and machine learning models in a production environment
• Strong experience in Python
• Hands-on Experience with end-to-end ML pipelines including data ingestion, preprocessing, feature engineering, model training, evaluation, deployment, and monitoring
• Cloud Platform Proficiency: Experience working with cloud providers like AWS or Azure, particularly with ML-focused services
• Strong API Integration Skills: Ability to build and consume RESTful APIs to integrate ML models with software applications
• Model Deployment Tools: Familiarity with containerization (Docker), orchestration (Kubernetes), and CI/CD for ML
• Solid Understanding of Statistical Modeling: Experience developing, validating, and interpreting statistical models and drawing insights from data
• Software Engineering Foundations: Knowledge of version control (Git), software development life cycle (SDLC), and agile development methodologies like SCRUM

Responsibilities

• Build and Deploy ML Solutions: Design, develop, and deploy end-to-end machine learning models and pipelines that automate data-driven decision-making and operational tasks
• Integrate ML with Applications and Workflows: Develop and integrate APIs and services to support machine learning applications
• Shape ML Architecture: Influence the design and technical architecture of ML systems, ensuring scalability, robustness, and maintainability
• Cross-Functional Collaboration: Partner with software engineers, product managers, and other stakeholders to translate business problems into ML solutions
• Model Lifecycle Management: Own and/or advise on the full lifecycle of machine learning models, from data preparation and model training to deployment and ongoing monitoring
• Data Preparation and Feature Engineering: Acquire, clean, and preprocess structured and unstructured data to ensure high-quality inputs for training statistical and machine learning models

Preferred Qualifications

• Client-Facing Experience: Experience in a consulting, technology, or law firm environment is a plus, especially working with non-technical stakeholders
• AI/ML Thought Leadership: Demonstrated ability to articulate and drive ML strategy and business value, ideally with examples of real-world impact
• Statistical and Predictive Modeling: Prior experience with advanced statistical modeling techniques, such as time series forecasting, regression, classification, or clustering
• Familiarity with AI Governance: Understanding of operational and legal considerations in deploying ML systems, especially in regulated environments

Benefits

• Six national health medical plans to choose from, including a HSA option
• Dental & Vision options
• Retirement Savings with a Safe Harbor 401K plan with immediate vesting and company match
• Long and short term disability coverage options
• Life Insurance and travel insurance
• Flexible PTO policy and 11 paid holidays
• Reimbursement for certifications related to your role
• Opportunity for career development, advancement and learning