We seek out curious minds. We value attention to detail, and we care deeply about outcomes. We re looking for passionate people, eager to learn, willing to share, establishing innovative ways of working and influencing culture change. Challenges are numerous and exciting. You will benefit from working with vibrant and diverse teams of IM, developing your skills through extensive dedicated training programs, the opportunity to travel and you will be empowered to make the difference!

This job requires the constant awareness of the compliance risks we face in day-to-day responsibilities. Continuous commitment to act with integrity with each other, with your communities, business partners and suppliers is the foundation of your success and sustainable growth.

Qualification & Experience

Bachelors or Masters degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, or a related field.
Proven experience working with large language models (LLMs).
Demonstrated experience building and deploying retrieval augmented generation (RAG) solutions.
Strong understanding of prompt engineering techniques.
Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow.
Experience with generative AI frameworks like LangChain.
Familiarity with cloud computing platforms (e.g., AWS, GCP, Azure).
Drive technical discussions and explain options/choices to technical and non-technical audience

Optional Skills

Hands-on experience with AI-related cloud services like AWS SageMaker, Amazon Bedrock, or Google Vertex AI.
Experience with multi-modal models.
Experience in Computer Vision.
MLOps experience (e.g., model deployment, monitoring, and continuous integration/continuous delivery).
Contributions to open-source projects related to artificial intelligence or machine learning.

Responsibilities

Develop and deploy generative AI applications, focusing on LLMs and RAG.
Design and implement efficient prompt engineering strategies to optimize LLM performance.
Build and maintain robust data pipelines for training and evaluating generative AI models.
Conduct experiments and analyze results to improve model performance and address business needs.
Collaborate with cross-functional teams (engineering, business, etc.) to integrate generative AI solutions into existing products or create new ones.
Stay up-to-date with the latest advancements in generative AI and related fields.
Contribute to the development of best practices and standards for generative AI development within the organization.
Clearly communicate complex technical concepts to both technical and non-technical audiences.

Success Metrics

Success will be measured in a variety of areas, including but not limited to

Delivering solutions on time, meeting large-scale enterprise standards and quality requirements
Bring innovative and cost effective solutions
Achieve customer satisfaction

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Data Scientist - Generative AI