AI Technical Lead

As an AI Technical Lead at Sayari, your primary responsibilities include developing proof of concepts (POCs) for AI solutions, overseeing their implementation and deployment, and continuously optimizing solution performance. You'll collaborate with development teams to ensure alignment with customer requirements while staying current with innovation in AI technologies. Your role is pivotal in taking ideas from research to productization effectively.

To qualify for the AI Technical Lead position at Sayari, candidates should have a Bachelor's degree in Computer Science or a related field, alongside a minimum of 1 year experience with Generative AI models and at least 4 years in programming using Python and JVM languages. Furthermore, familiarity with technologies such as SQL, Databricks, and ETL pipelines is essential for success in this role.

Sayari is deeply committed to employee development and offers limitless growth and learning opportunities for its AI Technical Lead. Employees are encouraged to engage in training programs that foster creativity and innovation. Plus, with a collaborative culture and access to cutting-edge AI technologies, Sayari provides the tools necessary for continual professional advancement.

The target salary for the AI Technical Lead position at Sayari ranges from $170,000 to $200,000 annually, complemented by a competitive benefits package, including quarterly bonuses and equity options. Keep in mind that final compensation offers are influenced by various factors such as experience, location, and internal equity.

The work culture at Sayari is collaborative, dynamic, and supportive. As an AI Technical Lead, you'll work alongside high-performing teams that value diverse perspectives and innovative approaches. Sayari fosters a workplace where employees' contributions are recognized and encouraged, making it an ideal environment for those looking to make a meaningful impact in AI technology.

To effectively develop proof of concepts for AI solutions, I begin by understanding the specific challenges faced by stakeholders and the desired outcomes. I then gather relevant data and evaluate existing models before creating initial prototypes. It’s essential to involve stakeholders throughout the process, ensuring that feedback is integrated for future iterations.

Absolutely! I have over a year of hands-on experience with Generative AI models, including large language models (LLMs). My work has involved fine-tuning these models to enhance their performance for specific applications, while ensuring compliance with best practices and addressing the nuances of prompt engineering.

I ensure deployment alignment with quality standards through comprehensive testing protocols and by involving cross-functional teams in the assessment framework. This collaboration allows us to maintain rigorous quality assurance checks, ensuring the AI solutions meet both performance benchmarks and customer expectations.

Optimizing AI solution performance involves continuous monitoring and iterative testing. I focus on controlling model drift by regularly re-evaluating algorithms and making necessary adjustments. Additionally, conducting infrastructure assessments allows me to identify bottlenecks and enhance efficiency.

Prioritizing tasks in AI projects requires clear communication and a deep understanding of project requirements. I typically assess the urgency and impact of each task, balancing short-term deliverables with long-term goals. Using project management tools also helps me maintain transparency and accountability across the team.

To stay updated with the latest AI technologies, I actively engage with academic journals, attend industry conferences, and participate in online forums and workshops. Networking with other professionals in the field allows me to share insights and gain fresh perspectives on emerging trends.

Effective communication is vital in my role, as it helps to bridge the gap between technical and non-technical audiences. I strive to convey complex AI topics in a clear manner, enabling stakeholders from various backgrounds to engage meaningfully in the development process.

Certainly! One challenging AI project involved developing an adaptive model in response to fluctuating business environments. It required a thorough analysis of data patterns and extensive collaboration with business units to redefine objectives. Through iterative testing and resilience, we were able to deploy a solution that greatly enhanced our operational efficiency.

Fine-tuning AI models typically involves adjusting hyperparameters and retraining the model on specific datasets to enhance its performance for particular applications. I often test various techniques such as transfer learning and cross-validation to yield optimal results.

Balancing innovation with practical applications in AI requires a focus on feasibility and user needs. I advocate for exploring creative solutions while remaining grounded in real-world constraints. This approach ensures that while we are innovating, the results are tangible and beneficial for our stakeholders.