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Machine Learning Engineer
4 weeks ago
BMC is looking for a Data Science, an innovator at heart, to join a team of highly skilled software developers, responsible for BMC's Helix Capacity Optimization product.
Here is how, through this exciting role, YOU will contribute to BMC's and your own success:
- The Senior Product Developer for AI/ML will play a crucial role in designing, developing, and implementing advanced AI and machine learning models into our products and services. This role involves working in collaboration with other teams, refining AI/ML solutions, and contributing to the organization's innovation through AI/ML technologies. Responsibilities include:
- Design, develop, and maintain high-quality software applications using Java and/or Python.
- Collaborate with cross-functional teams to understand requirements and translate them into technical specifications.
- Develop and implement software solutions that leverage GPT, LLM, and conversational AI technologies.
- Integrate GPT and LLM models into the software architecture to enable natural language understanding and generation.
- Develop conversational AI systems and chatbots that provide intuitive and human-like interactions.
- Implement and maintain APIs and microservices to enable seamless integration of conversational AI functionality into existing applications.
- Conduct thorough testing and debugging to ensure the quality, reliability, and security of the software applications.
- Stay updated on the latest advancements in GPT, LLM, and conversational AI technologies, and propose innovative solutions and improvements.
- Collaborate with product managers and stakeholders to define and prioritize features and enhancements.
- 5+ relevant years of Experience in python, data science.
- 5+ years of experience using ML to solve Natural Language Processing (NLP) use cases
- Good understanding of machine learning algorithms, tools and platforms
- Experience in toolkits such as NumPy, SciPy, scikit-learn, TensorFlow, PyTorch, Keras, Genism, etc. PyTorch is a must.
- Working knowledge of Word2Vec, NLTK, BERT
- Understanding of encoder and encoder-decoder model architectures.
- GPT based training will be a plus.
- Knowledge of the latest techniques in this space such as Lora adaptors, PEFT, quantization
- Experience in practical training cycles
- Experience with GPU usage
- Experience with inference accelerators
- Knowledge of fine tuning LLMs including BERT and GPT base.
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