Responsibilities

Translate business problems into ML solutions; build models for prediction, classification or recommendation; implement feature engineering, model training, hyperparameter tuning, evaluation and deployment.
Design and productionize AI and generative-AI solutions, including prompt engineering, retrieval-augmented generation, and multi-agent orchestration.
Create intelligent agents or tool-using workflows that automate decisions and enhance customer experiences.
Develop scalable data pipelines; Integrate experimentation and AI systems with modern data and MLOps platforms (e.g., Databricks, Mlflow), establish CI/CD pipelines, version control, testing and monitoring to ensure model quality and reliability.
Partner with software engineers, data engineers, product managers and subject-matter experts; present insights and recommendations to technical and non-technical stakeholders; translate complex analyses into clear narratives.
Research and apply emerging AI/ML techniques (generative AI, deep learning, agentic systems); contribute to improving team standards and mentoring junior team members.

Basic qualifications

3-5 years of professional experience as a data scientist or ML engineer; proven record of building and deploying ML models in production.
Master’s degree in computer science, Statistics, Mathematics, Engineering, Operations Research or a related quantitative field or Bachelor's degree with 5+ years of equivalent professional experience.
Strong programming skills in Python (plus experience in JavaScript), with proficiency in ML libraries (scikit-learn, PyTorch), data manipulation (pandas, SQL) and statistical analysis.
Hands-on experience with large language models (LLMs), generative AI, agentic systems, or AI-based product features.
Knowledge of MLOps tools and cloud platforms, especially Databricks (Spark, MLflow), AWS (S3, Redshift, SageMaker) or similar services.
Excellent communication skills; ability to explain complex technical concepts to both technical and business audiences and to collaborate effectively across teams.
Demonstrated ability to work independently on complex problems, manage multiple projects simultaneously, and deliver results in a fast-paced environment.

Advanced degree (master’s or PhD) in a relevant field (Statistics, Machine Learning, AI, etc.).
Exposure to industry-specific domains such as ecommerce, marketing analytics, risk/fraud, supply chain or logistics.
Fluency with big data frameworks (Spark, Hadoop), streaming systems, and container/orchestration tools (Docker, Kubernetes).
Knowledge of model explainability, interpretability techniques and responsible AI.

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Fulltime Ai Machine Learning Data Science Generative Ai