Applied AI ML Associate

Job Responsibilities

• Development and deployment of AI/ML solutions for Client Service Function.

• Design, develop, and deploy state-of-the-art AI/ML/LLM/GenAI solutions to meet business objectives.

• Conduct thorough evaluations of Gen AI models, iterate on model architectures, and implement improvements to enhance overall performance across applications.

• Develop appropriate functional, non-functional, and performance testing frameworks for models.

• Work directly with the US Core Client Service AI team on a daily basis to drive AI implementations from India.

• Stay updated with the latest trends and advancements in data science, machine learning, and related fields, and actively seek opportunities to enhance skills and knowledge.

• Adds to team culture of diversity, equity, inclusion, and respect.

Required Qualifications, Capabilities, And Skills

• Formal training or certification on Computer Science, Engineering, or a related field concepts and 3+ years applied experience.

• Experience in programming skills like Python

• Hands-on experience in applied AI/ML engineering with a track record of developing and deploying business-critical machine learning models and applications in production.

• Experience leading developers in a high impact fast paced environment.

• Proficient in programming languages like Python for model development, experimentation, and integration with Azure OpenAI API.

• Ability to identify and address AI/ML/LLM/GenAI challenges, implement optimizations, and fine-tune models for optimal performance in NLP applications.

• Strong collaboration and communication skills to work effectively with geographically spread cross-functional teams, communicate complex concepts, and contribute to interdisciplinary projects.

• Strong problem-solving and analytical skills with emphasize on attention to detail.

• Experience with cloud platforms, for deploying and scaling AI/ML models.

Preferred Qualifications, Capabilities, And Skills

• Experience in backend development, including databases (SQL/NoSQL/Graph), programming languages (Python/Java/Node.js), web frameworks, APIs, and microservices and possess front-end development skills.

• Knowledge of large language models (LLMs) and accompanying toolsets the LLM ecosystem (e.g. Lang chain, Vector databases, opensource Models like Mistral, Llama, etc)

• Exposure to cloud automation technologies such as Terraform

• Assess and choose suitable LLM tools and models for diverse tasks including but not limited to curating custom datasets and fine-tune LLM with a focus on parameter-efficient, mixture-of-expert, and instruction methods designing and developing advanced LLM prompts, Retrieval-Augmented Generation (RAG) solutions, and Intelligent agents for the LLMs and executing experiments to push the capability limits of LLM models and enhance their dependability.

Industries:Banking, Financial Services, Investment Banking