Data Scientist (AI & ML)

SUMMARY:
Are you ready to shape the future of Generative AI? This is your opportunity to join a visionary team where your mastery in AI, data engineering, and machine learning isn’t just valued, it’s mission-critical. If you’re passionate about building and deploying GenAI solutions that redefine how businesses operate, then this role was built for you.

POSITION INFO:
Our client is a leading, innovative, and driven organization, pioneering advanced AI capabilities across their ecosystem. They are searching for a Data Scientist – Generative AI to drive end-to end AI initiatives, from LLM development and RAG frameworks to scalable deployment and data pipeline engineering. This isn’t support work, it’s transformation at an enterprise level.

Key Responsibilities:

• Design, develop, and optimize Generative AI models for operational and analytical use
• Build and fine-tune RAG frameworks using tools like LangChain, LlamaIndex, and other LLM platforms
• Engineer scalable data pipelines and perform ETL to support AI model training and deployment
• Collaborate with data and DevOps teams to deploy AI solutions in production environments
• Integrate data from multiple sources and ensure robust data warehousing and accessibility
• Drive performance, scalability, and cost efficiency for AI models in cloud and on-prem settings
• Communicate complex AI concepts to non-technical stakeholders and align work to business goals
• Continuously research, experiment, and stay ahead of GenAI trends and architectures

• 3+ years in AI/ML, with at least 1–2 years focused on Generative AI
• Strong background in data engineering, ETL processes, and pipeline development
• Hands-on experience with TensorFlow, PyTorch, and GenAI frameworks
• Proficiency in Python, SQL, Pandas, and NumPy
• Experience with cloud platforms (AWS, GCP, and Azure) and container tools (Docker and Kubernetes)
• Knowledge of MLOps practices, including model versioning, monitoring, and retraining
• Familiarity with big data and storage tools like Spark, Hadoop, and vector databases
• Excellent communication, teamwork, innovation mindset, and analytical problem-solving
• Bachelor’s Degree in Computer Science, Data Science, ML, or a related field (Master’s/PhD advantageous)
• Preferred Certifications:
  • AWS Certified Machine Learning / AI Practitioner
  • GCP ML Engineer or Cloud Engineer
  • Microsoft Azure AI Engineer / Data Science

• AI & Machine Learning
• Data Engineering
• Actuarial & Analytics
• Product & Model Development
• Pricing & Valuations
• Risk & Automation