Role Proficiency:

Under guidance from Senior ML Engineers develop ML models that provides accurate results with controls to solve the business problem identified using state of art techniques.

Outcomes:

      Executes relevant data wrangling activities related to the problem in order to create dataset       Conduct ML experiments to understand the feasibility and build baseline models to solve the business problem       Fine tune the baseline model for optimum performance       Test Models internally per acceptance criteria from business       Document relevant Artefacts for communicating with the business       Work with data scientists to deploy the models.       Work with product teams in planning and execution of new product releases.       Set OKRs and success steps for self/ team and provide feedback to goals for team members       Work with cross functional teams - business technology and product teams to understand the product vision; building ML solutions that provides value to the product

Measures of Outcomes:

      Selection of the appropriate approach to the problem       Number of successful deployments of the model with optimised accuracy for baseline model       Adherence to project schedule / timelines       Personal and team achievement of quarterly/yearly objectives (OKR Assignments HIG Stretch goals)

Outputs Expected:

Design to deliver Product Objectives:

Design ML solutions which are aligned to and achieve product objectives Define data requirements for the model building and model monitoring; working with product managers to get necessary data

Updated on state of art techniques in the area of AI / ML :

Perform necessary research using the latest and state of art techniques to design scalable approaches Explain the relevance of the technologies
its pros and cons to the product team; enabling accurate design experiences

Skill Examples:

     Technically strong with the ability to connect the dots      Ability to communicate the relevance of technology to the stakeholders in a simple relatable language      Curiosity to learn more about new business domains and Technology Innovation      An empathetic listener who can give and receive honest thoughtful feedback

Knowledge Examples:

      Expertise in machine learning model building lifecycle       Clear understanding of various ML techniques with appropriate use to business problems       A strong background of statistics and Mathematics       Expertise in one of the domains – Computer Vision Language Understanding or structured data       Experience in executing collaboratively with engineering design user research teams and business stakeholders       Experience with data wrangling techniques preprocessing and post processing requirements for ML solutions       Good knowledge python and deep learning frameworks like Tensorflow Pytorch Caffe       Familiar with the machine learning model testing approaches       A genuine eagerness to work and learn from a diverse and talented team

Additional Comments:

Who we are: At UST, we help the world’s best organizations grow and succeed through transformation. Bringing together the right talent, tools, and ideas, we work with our client to co-create lasting change. Together, with over 30,000 employees in over 25 countries, we build for boundless impact—touching billions of lives in the process. Visit us at UST.com. Responsibilities: • Responsible for developing, deploying, and maintaining a Retrieval Augmented Generation (RAG) model in Amazon Bedrock, our cloud-based platform for building and scaling generative AI applications. • Design and implement a RAG model that can generate natural language responses, commands, and actions based on user queries and context, using the Anthropic Claude model as the backbone. • Integrate the RAG model with Amazon Bedrock, our platform that offers a choice of high-performing foundation models from leading AI companies and Amazon via a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI. • Optimize the RAG model for performance, scalability, and reliability, using best practices and robust engineering methodologies. • Design, test, and optimize prompts to improve performance, accuracy, and alignment of large language models across diverse use cases. • Develop and maintain reusable prompt templates, chains, and libraries to support scalable and consistent GenAI applications. Skills/Qualifications: • Experience in programming with at least one software language, such as Java, Python, or C/C++. • Experience in working with generative AI tools, models, and frameworks, such as Anthropic, OpenAI, Hugging Face, TensorFlow, PyTorch, or Jupyter. • Experience in working with RAG models or similar architectures, such as RAG, Ragna, or Pinecone. • Experience in working with Amazon Bedrock or similar platforms, such as AWS Lambda, Amazon SageMaker, or Amazon Comprehend. • Ability to design, iterate, and optimize prompts for various LLM use cases (e.g., summarization, classification, translation, Q&A, and agent workflows). • Deep understanding of prompt engineering techniques (zero-shot, few-shot, chain-of-thought, etc.) and their effect on model behavior. • Familiarity with prompt evaluation strategies, including manual review, automatic metrics, and A/B testing frameworks. • Experience building prompt libraries, reusable templates, and structured prompt workflows for scalable GenAI applications. • Ability to debug and refine prompts to improve accuracy, safety, and alignment with business objectives. • Awareness of prompt injection risks and experience implementing mitigation strategies. • Familiarity with prompt tuning, parameter-efficient fine-tuning (PEFT), and prompt chaining methods. • Familiarity with continuous deployment and DevOps tools preferred. Experience with Git preferred • Experience working in agile/scrum environments • Successful track record interfacing and communicating effectively across cross-functional teams. • Good communication, analytical and presentation skills, problem-solving skills and learning attitude. What we believe: We’re proud to embrace the same values that have shaped UST since the beginning. Since day one, we’ve been building enduring relationships and a culture of integrity. And today, it's those same values that are inspiring us to encourage innovation from everyone, to champion diversity and inclusion and to place people at the centre of everything we do. Humility: We will listen, learn, be empathetic and help selflessly in our interactions with everyone. Humanity: Through business, we will better the lives of those less fortunate than ourselves. Integrity: We honour our commitments and act with responsibility in all our relationships. Equal Employment Opportunity Statement UST is an Equal Opportunity Employer. We believe that no one should be discriminated against because of their differences, such as age, disability, ethnicity, gender, gender identity and expression, religion, or sexual orientation. All employment decisions shall be made without regard to age, race, creed, colour, religion, sex, national origin, ancestry, disability status, veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other basis as protected by federal, state, or local law. UST reserves the right to periodically redefine your roles and responsibilities based on the requirements of the organization and/or your performance. • To support and promote the values of UST. • Comply with all Company policies and procedures