- Develop and deploy advanced machine learning models to address business problems across multiple domains.
- Analyze large datasets, perform exploratory data analysis (EDA), and derive actionable insights.
- Build, test, and refine predictive models and algorithms using a variety of ML techniques (e.g., supervised and unsupervised learning, deep learning,computer vision).
AI & Gen AI Expertise:
- Apply cutting-edge generative AI models (e.g., GPT, VAEs, GANs) to create innovative solutions and products.
- Work with AI agents to automate and optimize workflows across different areas of the business.
- Explore and implement novel AI methodologies, including reinforcement learning and transfer learning.
Cloud Infrastructure:
- Design and implement scalable data pipelines and ML models in cloud environments such as AWS or Azure.
- Collaborate with the cloud infrastructure team to ensure smooth deployment and operation of models in production.
- Leverage cloud-native AI and ML tools (e.g., AWS SageMaker, Azure ML) to accelerate model development and deployment.
Collaboration & Leadership:
- Work closely with cross-functional teams, including software engineers, data engineers, and product managers, to integrate AI solutions into products.
Stay Up-to-date with Trends:
- Continuously research and evaluate the latest AI advancements, trends in machine learning, and emerging technologies in the AI space.
- Contribute to internal knowledge-sharing, promoting the latest findings, tools, and techniques to improve team capabilities.
Key Requirements:
Education:
- Bachelors in Computer Science, Data Science, Engineering, Mathematics, or a related field.
Experience:
- 5+ years of relevant hands-on experience as a Data Scientist or Machine Learning Engineer.
- Proven track record of developing and deploying machine learning models in a production environment.
- Experience with generative AI models (GPT, GANs, VAEs) and AI agents is a must.
- Strong knowledge of cloud platforms (AWS, Azure) and their AI/ML services (SageMaker, Azure ML, etc.).
- Solid understanding of the latest trends in AI, including large language models, reinforcement learning, computer vision & deep learning.
Technical Skills:
- Proficiency in Python, R, or other relevant programming languages.
- Strong knowledge of machine learning libraries such as TensorFlow, PyTorch, Scikit-learn, or Keras.
- Experience with SQL and cloud-native data processing tools (e.g., AWS Redshift, Azure Synapse, Spark).
- Familiarity with DevOps practices and CI/CD pipelines for ML model deployment.
Soft Skills:
- Strong communication skills with the ability to translate complex technical concepts into business-friendly language.
- Problem-solving mindset, with the ability to approach challenges creatively and collaborate with diverse teams.
- Leadership potential or experience mentoring junior team members.
Preferred Qualifications:
- Certification or training in AWS (e.g., AWS Certified Machine Learning), Azure, or other cloud services.
- Experience working with containerization technologies like Docker and Kubernetes for model deployment.
- Exposure to the latest trends in AI ethics, explainability, and fairness.
