Take the ownership of AI /ML use cases, from design and implementation to continuous enhancement.
Serve as a technical specialist on AI, with more focus on Gen AI, within a data science team. Design and develop solutions to improve advisors sales journey and optimize operational efficiency.
Collaborate with other data engineers, analysts, data scientists, product specialists, and other stakeholders to build well-crafted, pragmatic, and robust solutions that meet business requirements.
Stakeholder management and engagement. Proactively engage with stakeholders to understand their needs and translate them into technical requirements for AI / ML modelling.
Maintain documentation of dataset curation, modelling approach, model performance, code changes, and workflows.
Demonstrate a strong understanding of data privacy regulations such as PDPA, and AI governance guidelines to ensure compliance.
Foster an innovative and growth-oriented mindset, continuously seeking opportunities to enhance AI /ML models and drive improvements across the organization.
Requirements:
3~5 years of experience in data science role with relevant experience in AWS platform.
A bachelor’s degree in computer science or equivalent.
Familiarity with techniques for Document Chunking, Embedding, Information Retrieval for improving model accuracy and relevance.
Expertise in Prompt Engineering for designing and managing effective prompts.
Hands-on experience with AWS Bedrock, including deploying solution using foundation models (e.g., Anthropic Claude, Amazon Titan, Meta Llama) and integrating them into scalable applications using APIs and orchestration tools.
Experience in designing and applying evaluation frameworks for Gen AI models, including metrics and human-in-the-loop evaluation. Able to implement validation process and guardrails to mitigate risks such as hallucination and misinformation, ensuring the reliability and trustworthiness of AI-generated outputs.
Experience in Agentic Flow for maximizing the utility of models, including understanding user intent and context to drive meaningful interactions.
In depth knowledge of supervised and unsupervised ML Models – linear & logistic regression, clustering, tree-based models like random forest, bagging and boosting models.
In depth knowledge on feature engineering techniques, hyperparameter tuning and model evaluation.
Proficient in SQL, Python, and Spark.
Proven experience in implementing MLOps practices on AWS.
Familiar with Data Warehouses such as Redshift, BigQuery, Snowflake, Hive, and S3.
Passionate about technology and always looking to upskill based on new developments in AI space.
AWS certifications will be a plus.
Experience in the financial industry, telecommunications, or consulting is preferred.
