We are seeking an AI Engineer to join the DPS Engineering team, embedded within a cross-functional product delivery squad. This role will focus on designing, building, and deploying AI-driven capabilities that enhance product features, improve operational efficiency, and support data-driven decision-making.
The individual will work closely with product managers, engineers, and domain experts to translate business requirements into scalable AI solutions, ensuring seamless integration into production systems.
1. AI Solution Development
- Design, develop, and deploy machine learning and AI models (e.g., NLP, predictive analytics, GenAI use cases) aligned with product objectives
- Translate business problems into AI/ML solutions with clear success metrics
2. Product Integration
- Embed AI capabilities into product features and workflows within the delivery squad
- Collaborate with backend/frontend engineers to ensure scalable and reliable deployment
3. Model Lifecycle Management
- Build and maintain end-to-end ML pipelines (data ingestion, training, evaluation, deployment, monitoring)
- Ensure continuous improvement through model retraining and performance tuning
4. Data & Engineering Collaboration
- Work with data engineers to define data requirements, pipelines, and data quality standards
- Ensure proper feature engineering and dataset governance
5. Risk, Governance & Responsible AI
- Ensure AI solutions comply with enterprise standards on security, privacy, and responsible AI usage
- Document models, assumptions, and limitations clearly
Requirements:
- Strong experience in Python and AI/ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn)
- Experience with GenAI / LLMs (e.g., prompt engineering, RAG architectures, API-based models)
- Familiarity with MLOps practices (CI/CD, model deployment, monitoring)
- Experience working with cloud platforms (Azure preferred, AWS/GCP acceptable)
- Knowledge of data engineering concepts (SQL, data pipelines, APIs)
- Proven track record of delivering AI solutions in production environments
- Experience working in agile, squad-based delivery models
- Strong problem-solving mindset with a focus on business impact
- Ability to work in fast-paced, iterative delivery environments
- Effective collaboration across engineering, product, and business teams
- Curiosity and drive to continuously learn emerging AI technologies
