AI Engineer

Job Description

Must Have Skills

Solution Design & Development: Collaborate with stakeholders to turn business challenges

into technical solutions underpinned by generative AI, leveraging multi-modal LLMs that

encompass voice, text, and vision capabilities, including running models locally.

Prototyping Tools: Utilize tools such as Gradio and Flutter for rapid prototyping and

demonstration of AI solutions, focusing on seamless integration of multi-modal inputs and

outputs.

Performance Tuning & Benchmarking: Optimize and improve the performance of AI

solutions by implementing best practices (re-ranking, indexation). Conduct benchmarking to

test and validate the effectiveness and efficiency of AI models and solutions, ensuring they

meet required standards for both prototypes and full-scale implementations.

Vendor Management: Act as a Subject Matter Expert (SME) in vetting and evaluating vendor

solutions to ensure alignment with project goals and the integrity of complex AI

implementations, ensuring the effectiveness of multi-modal LLM utilization.

Guardrails and Ethics: Maintain an awareness of AI/GenAI ethics, guardrails, and principles,

ensuring solutions are developed responsibly and ethically, especially in handling sensitive

multi-modal data.

Stakeholder Engagement: Engage with stakeholders to ensure alignment of technical

solutions with business needs, facilitating feedback and iteration throughout the development process, and tailoring multi-modal capabilities to specific user requirements.

Continuous Learning & Research: Stay abreast of the latest developments in AI/GenAI

technologies, including advancements in multi-modal LLMs, enhancing solutions through

cutting-edge research and innovation.

Solution Design & Development: Collaborate with stakeholders to turn business challenges

into technical solutions underpinned by generative AI, leveraging multi-modal LLMs that

encompass voice, text, and vision capabilities, including running models locally.

Prototyping Tools: Utilize tools such as Gradio and Flutter for rapid prototyping and

demonstration of AI solutions, focusing on seamless integration of multi-modal inputs and

outputs.

Performance Tuning & Benchmarking: Optimize and improve the performance of AI

solutions by implementing best practices in model tuning. Conduct benchmarking to test and validate the effectiveness and efficiency of AI models and solutions, ensuring they meet

required standards for both prototypes and full-scale implementations.

Cloud Infrastructure: Utilize both GCP and AWS cloud stacks for development and

deployment, employing Python, Jupyter notebooks, and relevant technologies for managing

large-scale multi-modal data.

Vendor Management: Act as a Subject Matter Expert (SME) in vetting and evaluating vendor

solutions to ensure alignment with project goals and the integrity of complex AI

implementations, ensuring the effectiveness of multi-modal LLM utilization.

Guardrails and Ethics: Maintain an awareness of AI/GenAI ethics, guardrails, and principles,

ensuring solutions are developed responsibly and ethically, especially in handling sensitive

multi-modal data.

Agile Development: Use Jira for task and project management, ensuring agile methodologies are followed to streamline development processes and enhance collaboration on multi-modal projects. process, and tailoring multi-modal capabilities to specific user requirements

Continuous Learning & Research: Stay abreast of the latest developments in AI/GenAI technologies, including advancements in multi-modal LLMs, enhancing solutions through cutting-edge research and innovation