Senior AI Engineer

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Requirements:

• 4-6+ years in a similar Machine Learning or Data Scientist role, including 2+ years experience in LLMs.
• Proven experience as the first ML engineer or a similar role, demonstrating a strong ability to build ML and AI systems from the ground up.
• Demonstrated experience building AI apps in production.
• Proficiency in machine learning frameworks and libraries (e.g. Tensorflow, PyTorch, scikit-learn, Pandas)
• Experience in building with LLMs such as GPT, Claude, Llama etc and strong understanding of LLM architectures and tools (Llamaindex, vector databases, Transformers, Langchain etc)
• Experience with ETL/ELT tools, Data Lakehouse tech (Databricks, Python, Apache Spark, Hive, Parquet) and advanced SQL knowledge.
• Strong programming skills in Python and familiarity with additional languages and tools commonly used in ML engineering.
• Comfortable leading by example and using influence to drive collaboration, documentation, and knowledge sharing across teams and with a broad range of stakeholders.
• Able to demonstrate initiative, work independently, and thrive with autonomy while collaborating across teams in a culture of priority setting and moving forward with urgency in alignment with our organizational strategy
• Adept at focusing on multiple competing priorities, solving unique and complex technical problems, and persistently resolving blockers to progress
• Familiar with DevOps and MLOps principles such as design for manageability and root cause analysis
• Familiar working within leading software development best practices such as scrum/kanban, CI/CD, and test automation
• A strong driver to stay ahead of the curve with GenAI research and apply those insights to build real-world applications.

Responsibilities:

• Develop and refine autonomous agents leveraging generative AI to automate and streamline user workflows, enhancing operational efficiency and user experience.
• Design, create, evolve, and maintain scalable and efficient machine learning systems including, data pipelines, model training, deployment, and monitoring frameworks.
• Integrate and leverage Large Language Models (LLMs) to develop advanced NLP features, including but not limited to chatbots, workflow automation agents and data analysis tools using state-of-the-art models (e.g. OpenAI, Anthropic, open source models).
• Develop and enhance systems to deliver personalized experiences to our users, utilizing advanced machine learning and AI technologies to derive engagement and satisfaction.
• Partner across Product and Engineering teams on requirements to create product capabilities that fundamentally rely on AI and Machine Learning.
• Drive conversations within Engineering to improve and optimize the source data models, integration of the ML capabilities including those in our product platform.
• Identify, design, and implement internal process improvements, including automation for data quality control and data validation, improved data delivery, and scalability.
• Mentor other engineers, imparting best practices and institutionalizing efficient processes to foster growth and innovation within the team.