AI, Developer

Role overview

Model development and deployment

• Design, develop, train, and optimize machine-learning models for real use cases.
• Translate business and product requirements into scalable ML and AI solutions.
• Apply feature engineering, model selection, tuning, and evaluation techniques.
• Deploy production models for high availability and performance and optimize them for latency, throughput, and cost.

Pipelines and model operations

• Build and maintain model training, validation, deployment, and monitoring pipelines.
• Monitor model performance, data drift, and model decay; retrain models when needed.
• Ensure models meet reliability, scalability, and security standards.
• Implement experimentation frameworks such as A/B testing and offline evaluation.

Collaboration and responsible AI

• Work with data scientists, product managers, software engineers, and data engineering teams.
• Participate in design and code reviews and follow engineering best practices.
• Apply responsible AI principles, including fairness, explainability, and governance where required.

Experience and technical qualifications

• At least four years of hands-on machine-learning or applied-AI experience.
• Strong programming skills in Python and/or Java or Scala; understanding of algorithms, data structures, and software engineering principles.
• Solid knowledge of supervised and unsupervised algorithms and deep learning; framework experience such as TensorFlow, PyTorch, or Scikit-learn.
• Experience deploying models with Docker, Kubernetes, or cloud ML services and experience with AWS, Azure, or GCP.
• Hands-on experience with MLOps tools such as MLflow, Kubeflow, Airflow, SageMaker, or Azure ML.
• Experience with big data technologies such as Spark, Kafka, or Databricks and background in NLP, computer vision, or generative AI.
• Experience working in agile cross-functional teams; strong problem-solving, analytical, communication, and collaboration skills.

Listed tools and technologies

• The posting lists Dataiku, SQL Server, Power BI, and AWS Bedrock.