Data Scientist ( AI Specialist )

About the Role

Key Responsibilities

• Design, implement, and optimize operational and analytical solutions utilizing Generative AI models.
• Develop and fine-tune RAG (Retrieval Augmented Generation) frameworks utilizing LLM frameworks such as LangChain, Llama-Index or other generative frameworks.
• Experiment with new architectures and techniques to push the boundaries of generative AI.
• Collaborate with data engineering/analyst teams to ensure the availability and quality of data needed for model training and validation.
• Design and maintain scalable data pipelines to handle large volumes of structured and unstructured data.
• Perform ETL tasks with to create optimal data-warehousing structures (Dimensional modelling) for optimal storage, ease of use and maintainability.
• Integrate data from various sources and ensure its proper storage, transformation, and accessibility.
• Deploy AI and generative AI models in production environments, ensuring they meet performance, scalability, and reliability requirements.
• Work with DevOps teams to automate the deployment and monitoring of AI models.
• Optimize model inference for performance and cost efficiency in cloud and on-premises environments.
• Collaborate with cross-functional teams, including data scientists, engineers, product managers, and business stakeholders, to align AI solutions with business objectives.
• Communicate complex AI concepts and findings to non-technical stakeholders.
• Stay up to date with the latest advancements in Generative AI and data engineering.
• Participate in conferences, workshops, and other professional development opportunities.
• Contribute to research publications and patent filings in the field of Generative AI.

Requirements

• 3+ years of experience in AI, with a focus on Generative AI (1-2 years).
• Proven experience in data engineering, including data pipeline development, ETL processes, and database management.
• Hands-on experience with deep learning frameworks (e.g., TensorFlow, PyTorch) and Generative AI frameworks.
• Experience with cloud platforms (e.g., AWS, GCP, Azure) and containerization technologies (e.g., Docker, Kubernetes).
• Proficiency in programming languages such as Python, SQL, and familiarity with data manipulation libraries (e.g., Pandas, NumPy).
• Strong knowledge of machine learning algorithms, neural networks, and generative models.
• Experience with data storage solutions (e.g., Hadoop, Spark, Vector databases).
• Knowledge of MLOps practices, including model versioning, monitoring, and retraining.
• A proactive attitude with a passion for learning and innovation.
• Excellent communication and teamwork abilities.
• Strong problem-solving and analytical skills.

Nice to Have

• Bachelor's or master's degree in Computer Science, Data Science, Machine Learning, or a related field.
• Master's or Ph.D. is advantageous.
• AWS Certified AI Practitioner
• AWS Certified Machine Learning
• GCP Professional Machine Learning Engineer
• GCP Associate Cloud Engineer
• Microsoft Azure AI engineer
• Microsoft Azure Data Science.

Qualifications

• Bachelor's or master's degree in Computer Science, Data Science, Machine Learning, or a related field.
• Master's or Ph.D. is advantageous.
• Any of the listed certifications are highly advised.