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Job Description
Trinnex, a wholly owned subsidiary of CDM Smith, is seeking a versatile, proactive, and highly collaborative Intermediate AI/ML Developer to join our team. We operate in a fast-paced, rapidly evolving environment where taking initiative and ownership is crucial. In this role, you will not just execute tasks, but actively identify opportunities, anticipate bottlenecks, and drive intelligent solutions across a diverse suite of products and services.
Because our team works on a wide array of dynamic initiatives, your focus will adapt depending on the active project or product—ranging from predictive modeling and deep learning to natural language processing and generative AI. Operating in an Agile environment, you will collaborate closely with cross-functional teams, transforming complex algorithms into production-ready code.
Key Responsibilities
Model Development & Engineering
• Design & Implement: Develop, modify, and improve AI/ML solutions across our product and service offerings, adapting to the unique needs of different projects.
• Algorithm to Code: Convert theoretical algorithms into clean, robust, and scalable code that leverages machine learning concepts to interpret large and complex datasets.
• Fine-Tuning & Customization: Customize, train, and validate machine learning or predictive models to meet specific end-user and business objectives.
• Innovation: Explore, evaluate, and implement cutting-edge techniques and methodologies to enhance product performance and develop new features.
Data Analysis & Performance
• Data Exploration: Conduct data mining, statistical analysis, and predictive modeling activities to identify patterns and trends within large datasets.
• Testing & Validation: Rigorously test and validate AI model accuracy and reliability to ensure they perform to technical and business specifications.
Agile Collaboration, Delivery & Initiative
• Sprint Execution: Active participation in Agile sprint planning, daily stand-ups, backlog grooming, and retrospectives to ensure project assignments are delivered on time with excellent quality.
• Proactive Problem Solving: Take initiative to identify system inefficiencies, propose solutions, and actively address technical issues or blockers before they impact delivery.
• Communication: Clearly and effectively communicate project status, blockers, and technical insights.
• Cross-Functional Teamwork: Collaborate with product managers, data engineers, and software developers to integrate models into production environments and resolve technical issues.
Documentation & Best Practices
• Technical Documentation: Create comprehensive documentation, including design patterns, technical specifications, and normalization processes to support data architecture and modeling efforts.
• Continuous Learning: Maintain active working and learning relationships with industry innovators and peer groups to keep our practices current.
Skills and Abilities:
• Strong proficiency in modern software development using Python (knowledge of C/C++, Java is a plus).
• Practical experience with popular machine learning and deep learning toolkits (e.g., Scikit-Learn, PyTorch, TensorFlow, Hugging Face).
• Solid capability in querying and manipulating large datasets using SQL and data processing libraries (e.g., Pandas, NumPy).
• Familiarity with cloud platforms (GCP preferred), model deployment practices (MLOps, Docker), and CI/CD pipelines is highly advantageous.
• A self-starter mindset with a strong drive to take ownership of tasks, troubleshoot issues independently, and actively contribute ideas to improve products and processes.
• Ability to thrive and remain agile in a fast-paced, highly dynamic environment, pivoting comfortably between different product focuses.
• Demonstrated experience working effectively within Agile/Scrum methodologies.
• Strong communication skills with the ability to bridge the gap between technical details and business outcomes.
• Analytical and critical thinking skills with a meticulous approach to model validation and code quality.
#LI-LP1
#LI-REMOTE
Because our team works on a wide array of dynamic initiatives, your focus will adapt depending on the active project or product—ranging from predictive modeling and deep learning to natural language processing and generative AI. Operating in an Agile environment, you will collaborate closely with cross-functional teams, transforming complex algorithms into production-ready code.
Key Responsibilities
Model Development & Engineering
• Design & Implement: Develop, modify, and improve AI/ML solutions across our product and service offerings, adapting to the unique needs of different projects.
• Algorithm to Code: Convert theoretical algorithms into clean, robust, and scalable code that leverages machine learning concepts to interpret large and complex datasets.
• Fine-Tuning & Customization: Customize, train, and validate machine learning or predictive models to meet specific end-user and business objectives.
• Innovation: Explore, evaluate, and implement cutting-edge techniques and methodologies to enhance product performance and develop new features.
Data Analysis & Performance
• Data Exploration: Conduct data mining, statistical analysis, and predictive modeling activities to identify patterns and trends within large datasets.
• Testing & Validation: Rigorously test and validate AI model accuracy and reliability to ensure they perform to technical and business specifications.
Agile Collaboration, Delivery & Initiative
• Sprint Execution: Active participation in Agile sprint planning, daily stand-ups, backlog grooming, and retrospectives to ensure project assignments are delivered on time with excellent quality.
• Proactive Problem Solving: Take initiative to identify system inefficiencies, propose solutions, and actively address technical issues or blockers before they impact delivery.
• Communication: Clearly and effectively communicate project status, blockers, and technical insights.
• Cross-Functional Teamwork: Collaborate with product managers, data engineers, and software developers to integrate models into production environments and resolve technical issues.
Documentation & Best Practices
• Technical Documentation: Create comprehensive documentation, including design patterns, technical specifications, and normalization processes to support data architecture and modeling efforts.
• Continuous Learning: Maintain active working and learning relationships with industry innovators and peer groups to keep our practices current.
Skills and Abilities:
• Strong proficiency in modern software development using Python (knowledge of C/C++, Java is a plus).
• Practical experience with popular machine learning and deep learning toolkits (e.g., Scikit-Learn, PyTorch, TensorFlow, Hugging Face).
• Solid capability in querying and manipulating large datasets using SQL and data processing libraries (e.g., Pandas, NumPy).
• Familiarity with cloud platforms (GCP preferred), model deployment practices (MLOps, Docker), and CI/CD pipelines is highly advantageous.
• A self-starter mindset with a strong drive to take ownership of tasks, troubleshoot issues independently, and actively contribute ideas to improve products and processes.
• Ability to thrive and remain agile in a fast-paced, highly dynamic environment, pivoting comfortably between different product focuses.
• Demonstrated experience working effectively within Agile/Scrum methodologies.
• Strong communication skills with the ability to bridge the gap between technical details and business outcomes.
• Analytical and critical thinking skills with a meticulous approach to model validation and code quality.
#LI-LP1
#LI-REMOTE