Must Haves:

• 5 years of full-time work experience in one or more relevant machine-learning roles.
• Proven experience as a Machine Learning Engineer or in a similar role.
• Experience with developing machine learning models at scale from inception to business impact
• Strong expertise in machine learning frameworks (e.g., TensorFlow, PyTorch) and programming languages (e.g., Python, R).
• Experience in working with large data sets, data processing, and data analytics.
• Familiarity with cloud platforms, preferably AWS, and their machine learning services.
• Strong problem-solving skills and ability to work in a fast-paced, team-oriented environment.
• Excellent communication and collaboration skills.
• Bachelor’s or master’s degree in computer science, Engineering, Data Science, or a related field.

Preferred Qualifications:

• Experience delivering models through the MLOps life cycle from exploration to serving.
• Familiarity with GenAI technology stack, including frameworks for prompt engineering, guardrails for GenAI applications, and LLM fine-tuning.
• Experience working with VectorDBs and other data infrastructure required to efficiently support Generative AI training pipelines and production applications.

JOB DESCRIPTION Seeking an experienced Machine Learning Engineer to join our team and play a pivotal role in the development of our advanced Generative AI applications and systems. This dynamic role involves a variety of initiatives, including designing, building, and delivering ML applications leveraging AI technologies like LLMs and FMs.
Key Responsibilities:

• Develop, train, and fine-tune ML models, with a focus on LLMs, FMs, and RAG.
• Lead the model development lifecycle from data collection and preprocessing to model evaluation and deployment.
• Collaborate with business partners, engineers, analysts, and data scientists to translate business requirements into effective AI solutions.
• Conduct experiments, including A/B testing, to enhance model accuracy and efficiency.
• Deploy models to production, ensuring scalability and efficiency.
• Optimize machine learning applications for performance and reliability.
• Monitor and maintain models in production environments.
• Stay updated on AI, ML, and GenAI advancements to drive innovation.
• Provide data-driven insights and recommendations.
• Mentor team members on AI/ML engineering best practices