Pioneering the future of Generative AI

The candidate will design, develop, and maintain software systems with integrated generative AI. The ideal candidate will have strong experience in software engineering, a focus on building production-ready systems, and experience with data engineering, and machine learning. The candidate should also have experience with large language models (LLMs) and be familiar with LLM libraries such as LangChain or LLamaindex.

Responsibilities

● Create prototypes and proofs of concept that demonstrate the feasibility and potential benefits of generative AI solutions

● Design, implement, and maintain scalable and robust generative AI systems that meet performance, cost-effectiveness, and latency requirements

● Evaluate the capabilities and limitations of generative AI models, including their accuracy, bias, and interpretability, and provide recommendations for their appropriate use

● Identify potential applications of generative AI for clients

● Provide expert advice and guidance to clients on the use of generative AI

● Keep up to date with the latest research and advancements in the field, stay informed about emerging trends and best practices, and contribute to the generative AI community

Requirements

● Strong experience in software engineering, with a focus on building production-ready systems

● Passion for learning new technologies and staying updated with the latest advancements in generative AI

● Ability to quickly adapt to changing requirements and evolving technologies

● Ability to clearly articulate technical concepts and ideas to both technical and non-technical audiences

● Experience with Python and Golang

● Experience with large language models (LLMs) and familiar with LLM libraries such as LangChain or LLamaindex is a plus

● Familiarity with cloud platforms such as Azure, AWS or GCP is a plus

● Familiarity with frontend technologies such as React is a plus

Responsibilities:

• Design, develop, and maintain scalable ETL pipelines to process structured data from various sources.

• Collaborate with AI engineers to define data requirements and ensure smooth data integration with Generative AI solutions and machine learning models.

• Build and optimize data models to support engineering and business needs, ensuring accuracy, consistency, and performance.

• Implement and enforce data quality standards, ensuring data is clean, validated, and up to date.

• Develop storage solutions that enable efficient querying, analysis, and reporting.

• Monitor, troubleshoot, and optimize data pipelines to ensure reliable and timely delivery of data.

• Stay informed on best practices and advancements in data engineering with a focus on extending data platforms with semi- and unstructured data.

Requirements

• Experience: Minimum of 5 years of hands-on experience working with enterprise-grade data platforms.

• Technical Skills:

  • Proficiency in SQL and PySpark and experience with large-scale data processing.

  • Strong command of common data engineering languages such as Python, Scala, or Java.

  • Familiarity with cloud-based data platforms like Databricks, Google BigQuery and DataProc, or AWS Redshift and EMR is a plus.

  • Proficiency in data modeling, including design and implementation of raw data layer (bronze), refined data layer (silver), business/consumption layer (gold)

  • Experience with ETL tools, data pipeline orchestration frameworks (e.g., Apache Airflow), and version control.

  • Knowledge of data warehousing concepts and best practices for large-scale data environments.

• Platform Development Interest: A strong interest in the full stack of platform development, including software engineering, machine learning, front-end development, and generative AI.

• Curiosity: A keen interest in expanding knowledge and skills in generative AI, with the motivation to work on cutting-edge AI-driven projects.

• Problem-Solving Mindset: Ability to solve complex data challenges in innovative and efficient ways.

Collaboration: Ability to work in a cross-functional environment, collaborating with a diverse team of engineers.