Intermediate AI/ML Developer
Listed on 2026-06-23
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Software Development
Machine Learning/ ML Engineer, AI Engineer (Applied/Software), Data Scientist
Why Trinnex? If you are passionate about water and technology, Trinnex is the place for you! Trinnex is a visionary company that is transforming the way water resources are managed and protected. By combining cutting‑edge digital technologies, such as sensor/IoT data, models, geospatial data, and AI/machine learning, we create innovative, smart, and scalable solutions that make a difference. Whether it's optimizing water supply and demand, detecting leaks and anomalies, or enhancing water quality and resilience, Trinnex delivers value and impact to public sector clients across the country.
Job DescriptionTrinnex, 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.
- 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.
- Design & Implement:
- 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.
- Data Exploration:
- 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.
- Sprint Execution:
- 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.
- Technical Documentation:
- 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, Tensor Flow, Hugging Face).
- Solid capability in querying and manipulating large datasets using SQL and data processing libraries (e.g., Pandas, Num Py).
- 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…
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