Sr. Machine Learning Engineer, Generative AI Applications

Job Description

• Architect, build, maintain, and improve new and existing suite of GenAI applications and their underlying systems.
• Automate machine learning pipelines, monitor performance and costs, and optimize models by using techniques such as LoRA/QLoRA.  
• Establish reusable frameworks to streamline model building, deployment and monitoring. Incorporate comprehensive monitoring, logging, tracing, and alerting mechanisms.
• Build guardrails, compliance rules and oversight workflows into the GenAI application platform, such as establishing approval chains for model updates and staged rollout for production releases.
• Develop templates, guides and sandbox environments for easy onboarding of new contributors and experimentation with new techniques.
• Ensure development of user-facing applications in the GenAI application platform is easy and safe by enforcing rigorous validation testing before publishing user-generated models and implement a clear peer review process of applications.
• Use your entrepreneurial spirit to identify new opportunities to optimize business processes, improve consumer experiences, and prototype solutions to demonstrate value.
• Work closely with data scientists and analysts to create and deploy new product features online and in mobile apps.
• Contribute to and promote good software engineering practices across the team.
• Mentor and educate team members to adopt best practices in writing and maintaining production machine learning code.
• Actively contribute to and re-use community best practices.
• Monitor, debug, track, and resolve production issues.
• Work with project managers to ensure that projects proceed on time and on budget.
• Collaborate with Technical Product Managers to ensure proper tracking of algorithmic performance KPIs and prioritize performance improvements based on effort and impact.
• Complete other responsibilities as assigned.

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Qualifications

• Minimum of seven years’ post-secondary education or relevant work experience.
• Bachelor's degree in mathematics, physics, computer science, engineering, statistics, or an equivalent technical discipline.
• Minimum of five years’ software development experience with Python and SQL.
• Minimum of three years’ experience building pipelines to deploy NLP and deep learning models into production in a cloud environment.
• Minimum of three years’ experience using PyTorch, Tensorflow, or MXNet, along with optimizing code for GPU clusters.
• Experience building advanced workflows such as retrieval augmented generation, model chaining, dynamic prompting, PEFT/SFT, etc. using LangChain and similar tools.
• Experience in establishing model guardrails and developing bias detection and mitigation techniques for AI applications using tools such as NeMo.
• Experience with various embedding models and setting up and tuning vector databases to improve performance of semantic search and retrieval systems.
• Understand the underlying fundamentals such as Transformers, Self-Attention mechanisms that form the theoretical foundation of LLMs.
• Experience in working with a variety of relational SQL and NoSQL databases, big data tools like Hadoop, Spark, Kafka; a Linux environment; (AWS).
• Knowledge of data pipeline and workflow management tools.
• Expertise in standard software engineering methodology, e.g., unit testing, test automation, continuous integration, code reviews, design documentation.
• Bachelor’s degree in computer science, Information Technology, or related field.