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We are committed to supporting both our employees and our communities. We understand that employees are our most valuable asset. To that end, we support work-life balance through a variety of programs, including remote work opportunities. We are committed to providing an even and reasonable workload in order to maintain a good work-life balance for our employees. We regularly sponsor or participate in charitable events to support our communities. Our corporate philanthropy program also rewards our employees for their personal contributions to non-profit organizations of their choice. Our corporate philanthropy program includes employee compensation for free time for volunteering, compensation for free time for blood donation, and a charity match program.

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We are proud to be an Equal Employment Opportunity employer and consider qualified applicants without regard to race, color, creed, religion, ancestry, national origin, sex, sexual orientation, gender identity or expression, age, disability, veteran status, or any other protected factor under federal, state or local law.
Multiple positions at different locations have come up with our top-tire Financial client. Forward your resume or you can also fill in your personal contact information. Every resume is assigned to a senior member of our recruitment team who will contact you directly.
AI / ML Engineer
Role Overview
  • As an AI Engineer, you will design and develop AI models, fine-tune Large language models, and bring innovative AI frameworks into the platform. You will play a critical role in building Graph Neural Networks, Complex Retrieval-Augmented Generation (RAG) systems, and implementing agentic workflows. Collaborating closely with data scientists, DevOps, and engineering teams, you will ensure seamless integration and scalability of AI-driven solutions across the platform.
Core Skills
  • Strong expertise in Graph Neural Networks, Transformers, and RAG systems.
  • Proficiency with machine learning frameworks such as:
  • PyTorch
  • TensorFlow
  • Hugging Face for NLP and multi-modal models.
  • Experience with frameworks such as:
  • LangChain
  • Ollama
  • GraphRAG (or similar)
Responsibilities
  • Develop and fine-tune machine learning models, including Graph Neural Networks.
  • Implement Graph-based Retrieval-Augmented Generation (RAG) systems to improve information retrieval and enhance generative model outputs.
  • Design, build, and refine the Knowledge Graph’s
  • Fine-tune LLMs using techniques such as Proximal Policy Optimization (PPO), or Parameter Efficient Fine Tuning
  • Leverage PyTorch, TensorFlow, and Hugging Face.
  • Research and integrate cutting-edge AI advancements into the platform.
  • Collaborate with data scientists, DevOps, and other engineers to deploy models into production environments.
  • Develop autonomous, self-learning agentic workflows to enhance platform automation.
  • Improve platform performance by testing and implementing new AI technologies.
  • Implement RAG systems to improve platform information retrieval and context awareness.
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