Artificial Intelligence Engineer

About the Company: As an AI Engineer with an expirence between 5years to 9 years, you'll be working with a global team of AI and machine learning practitioners, software engineers, and business and solution architects to implement state of the art - AI enabled digital solutions to transform the P&C insurance globally. This position offers the opportunity to develop, deploy, and optimize Large Language Models (LLMs).

About the Role: Focus on Finetuning, Building LLMs, keywords like PEFT, LORA, DPO, Multi-GPU Finetuning.

Responsibilities:

• Work closely with team of AI engineers to design, build, and serve LLMs to solve complex business challenges using Azure (CPU & GPU environments).
• Researching & implementing state of the art LLM techniques including pre-training, fine-tuning, preference alignment, and deployment while also focusing on prompt engineering and generative AI more broadly.
• Heavily focusing on developing novel data sets that enable LLMs to perform new tasks as well as tooling/platforming to collect these samples at scale.
• You will need strong python data fundamentals coupled with a software mindset for making data processing and collection pipelines repeatable, scalable, and high quality.
• Ensure high quality code that meets business objectives, quality standards and development guidelines.
• Building reusable pipelines, processes, and tools to streamline LLM and generative AI workflows.
• Manage project stakeholder expectations and issue communications on progress.
• React to shifting priorities without compromising deadlines and momentum.

Qualifications:

• Must have: 5-9 years experience in AI/ML.
• Experience in LLM Engineering – pretraining, post-training/alignment.
• Deep expertise in writing and reviewing production code in Python.
• Understanding the development lifecycle for LLMs— developing data sets for pre-training, instruction tuning, and preference alignment alongside the modelling techniques for each stage and LLM deployment is a major plus.
• Multi-disciplinary approach to problem solving, including excellent interpersonal and communication skills (written and verbal).
• This includes crisply talking about technical solutions while being able to collaborate with business architects effectively.
• Strong knowledge of LLM frameworks and libraries (such as transformers, trl, deepspeed, PyTorch), and exposure to various ML techniques and their practical implementation in production at large scale.
• Experience on distributed, high throughput and low latency architectures.
• Strong fundamentals in NLP techniques for text representation, semantic extraction techniques, data structures and modeling.
• Experience building software on top of major container technology (Kubernetes, Docker etc.).