About Our Client: Our client empowers organisations to gain a competitive edge through the power of Generative AI. They collaborate with large corporate clients to develop meaningful AI strategies, build production-ready AI solutions, and seamlessly integrate those solutions into their business operations.

Role Overview: As an Applied AI Solution Engineer, you will work in small, collaborative teams to design, build, and deploy AI applications, including agents based on Large Language Models (LLMs). The applications you create will typically leverage existing closed or open-source foundational models, with potential fine-tuning to meet specific client needs.

Key Responsibilities:

• AI Solution Development: Design and develop AI-powered solutions, particularly focusing on large language models and their applications.
• Client Consultation and Communication: Engage with clients to understand their needs, provide expert guidance, and communicate technical details effectively.
• Technical Problem-Solving: Address complex technical challenges and deliver robust, scalable solutions.
• Technical Leadership: Lead small teams in the development and deployment of AI solutions, ensuring best practices are followed.
• Quality Assurance and Testing: Implement rigorous testing protocols to ensure the reliability and performance of AI applications.
• Ethical Consideration and Compliance: Ensure all AI solutions adhere to ethical guidelines and regulatory requirements.

Ideal Candidate Profile:

• Strong hands-on experience in developing production-grade AI solutions, particularly those involving:
  • Building microservices, including scalable data pipelines using frameworks like Spark.
  • Data technologies such as Python and SQL.
  • Working with large language models, including fine-tuning both closed and open-source models (e.g., OpenAI API).
  • Solution design, especially data-driven applications using Python, SQL, and related technologies.
  • Analytical problem-solving skills, with a track record of addressing complex technical challenges.

About Our Client: Our client empowers organisations to gain a competitive edge through the power of Generative AI. They collaborate with large corporate clients to develop meaningful AI strategies, build production-ready AI solutions, and seamlessly integrate those solutions into their business operations.

Role Overview: As an Applied AI Solution Engineer, you will work in small, collaborative teams to design, build, and deploy AI applications, including agents based on Large Language Models (LLMs). The applications you create will typically leverage existing closed or open-source foundational models, with potential fine-tuning to meet specific client needs.

Key Responsibilities:

• AI Solution Development: Design and develop AI-powered solutions, particularly focusing on large language models and their applications.
• Client Consultation and Communication: Engage with clients to understand their needs, provide expert guidance, and communicate technical details effectively.
• Technical Problem-Solving: Address complex technical challenges and deliver robust, scalable solutions.
• Technical Leadership: Lead small teams in the development and deployment of AI solutions, ensuring best practices are followed.
• Quality Assurance and Testing: Implement rigorous testing protocols to ensure the reliability and performance of AI applications.
• Ethical Consideration and Compliance: Ensure all AI solutions adhere to ethical guidelines and regulatory requirements.

Ideal Candidate Profile:

• Strong hands-on experience in developing production-grade AI solutions, particularly those involving:
  • Building microservices, including scalable data pipelines using frameworks like Spark.
  • Data technologies such as Python and SQL.
  • Working with large language models, including fine-tuning both closed and open-source models (e.g., OpenAI API).
  • Solution design, especially data-driven applications using Python, SQL, and related technologies.
  • Analytical problem-solving skills, with a track record of addressing complex technical challenges.