The company is seeking an innovative AI & Data Science Specialist to lead the development of intelligent solutions leveraging Artificial Intelligence technologies, with a focus on Generative AI and advanced analytics. The successful candidate will transform data into valuable business insights and develop AI-powered applications that drive smarter decision-making, improve operational efficiency, and support digital transformation initiatives.
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Key Responsibilities
- Design, develop, train, and optimize machine learning models for real applications or use cases.
- Translate business and product requirements into scalable ML/AI solutions.
- Implement feature engineering, model selection, tuning, and evaluation techniques.
- Develop and deploy ML models into production environments with high availability and performance.
- Build and maintain ML pipelines (training, validation, deployment, monitoring).
- Monitor model performance, data drift, and model decay; retrain models as needed.
- Ensure models meet reliability, scalability, and security standards.
- Work closely with Data Scientists, Product Managers, and Software Engineers.
- Collaborate with data engineering teams to ensure high-quality, reliable data pipelines.
- Participate in design and code reviews, ensuring engineering best practices.
- Optimize models for latency, throughput, and cost.
- Implement experimentation frameworks (A/B testing, offline evaluation).
- Apply responsible AI principles, including fairness, explainability, and governance where required.
Requirements
- +4 years of hands-on experience in Machine Learning or applied AI roles.
- Strong programming skills in Python (and/or Java, Scala).
- Solid understanding of ML algorithms (supervised, unsupervised, deep learning).
- Experience with frameworks such as TensorFlow, PyTorch, Scikit-learn.
- Experience deploying models using Docker, Kubernetes, or cloud ML services.
- Strong knowledge of data structures, algorithms, and software engineering principles.
- Experience working in agile, cross-functional teams.
- Experience with cloud platforms (AWS, Azure, or GCP) and managed ML services.
- Hands-on experience with MLOps tools (MLflow, Kubeflow, Airflow, SageMaker, Azure ML).
- Experience with big data technologies (Spark, Kafka, Databricks).
- Background in NLP, Computer Vision, or Generative AI.
- Strong problem-solving and analytical thinking.
- Production-first mindset.
- Data-driven decision making.
- High collaboration and communication skills.
Tools & Technologies
- Dataiku
- SQL Server
- Power BI
- AWS Bedrock
Location
Client site
