Mid-Level
Machine Learning Engineer, GenAI/ML Associate
Join a dynamic and diverse team that engineers large-scale, complex, and resilient technology solutions that drive our global business. A career with us is a journey, not a destination. This could be the next best step in your technical career. Join us if you are excited about shaping the future of technology and driving significant business impact in financial services. Join our team and help us develop game changing, high-quality solutions. As a AI/ML Engineer Associate within our platform operations team, you will be tasked with the development, construction, and maintenance of our AIOps solution. This role demands acknowledge of AI/ML technologies, IT infrastructure, and platform engineering.
Job Responsibilities:
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Ability to design, development, and deployment of generative AI solutions, ensuring alignment with business objectives and technical requirements.
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Demonstrate expertise in generative AI technologies, contributing to the development of POCs and evaluating new methodologies to enhance AI capabilities.
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Exhibit strong proficiency in Java or Python, with the ability to architect and build complex AI models from scratch. Ensure the delivery of secure, high-quality production code.
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Utilize experience with React or Angular to create intuitive user interfaces for AI applications. Conduct thorough code reviews to maintain high standards of code quality.
Actively participate in communities of practice to promote the adoption and awareness of new generative AI technologies, fostering a culture of continuous innovation.
Required qualifications, capabilities, and skills:
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Minimum 2+ years of strong proficiency in Python or Java
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one year of experience in generative AI development and prompt engineering.
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Proven experience in application development, testing in AI projects.
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Minimum of 2+ years of experience with React or Angular.
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2+ years of AWS experience is a must-have.
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Experience with LLM models and agentic tools.
Preferred qualifications, capabilities, and skills:
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Experience with AI model optimization and performance tuning to ensure efficient and scalable AI solutions.
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Familiarity with data engineering practices to support AI model training and deployment.
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Strong understanding of machine learning algorithms and techniques, including supervised, unsupervised, and reinforcement learning.
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Experience with AI/ML libraries and tools such as TensorFlow, PyTorch, Scikit-learn, and Keras.
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