Machine Learning Engineer

We are seeking a highly skilled Machine Learning Engineer to join our team. The ideal candidate will have a strong background in Natural Language Processing (NLP), Large Language Models (LLMs), and Python programming. You will work closely with data scientists, product managers, and data engineers to design, develop, and deploy high-performance AI/ML models and integrate generative AI solutions into existing workflows.

Responsibilities not listed.

• Proficient in generative AI techniques, prompt engineering, and Retrieval-Augmented Generation (RAG) (2+ years)
• Experience with Large Language Models (LLMs) such as OpenAI, Gemini, LLAMA, and other state-of-the-art models (2+ years)
• Expertise in using ML/AI libraries such as Pandas, NumPy, PyTorch, TensorFlow, Keras, BERT, LayoutLM, and traditional ML algorithms (5+ years)
• Experience with distributed ML/AI training libraries/models: Koalas, Horovod, DDP
• Expertise in Pythonic clean coding practices, including the use of decorators, generators, and descriptors (5+ years)
• Strong understanding of software design principles such as DRY, OAOO, YAGNI, KIS, EAFP/LBYL, and defensive programming (2+ years)
• Proficient in software design concepts focusing on cohesion and coupling (2+ years)
• Knowledge of SOLID principles (2+ years)

• Collaborating with cross-functional teams to design and deliver high-performance AI models
• Integrating generative AI solutions into existing workflow systems
• Developing and maintaining the ML Operations CI/CD pipeline for seamless deployment and monitoring
• Training, tuning, and optimizing AI models and algorithms for enhanced performance
• Implementing complex real-time data and AI/ML applications to capture knowledge and automate decision-making processes
• Creating ML/AI models for business teams and establishing metrics to track their accuracy and performance
• Overseeing the full lifecycle of algorithm development, from ideation to deployment and monitoring
• Evaluating and ranking ML algorithms based on their potential success in solving specific problems
• Serving as an internal resource for AI/ML needs, providing guidance and insights to stakeholders during strategic discussions

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