• You are determined to stay ahead of the latest technologies in Data Science!
• We’re one of the largest and most advanced Data Science practices in the country
• Together we can build state-of-the-art Data Science models that power seamless experiences for millions of customers
• Please note that we have two opportunities

TECH SKILLS:

We use a broad range of tools, languages, and frameworks. While we don’t expect you to know them all, experience or exposure to any ML systems and statistical modelling will set you up for success in this role!

• Proven delivery of retrieval-augmented-generation (RAG) pipelines, including vector-database design (FAISS, Pinecone, Amazon OpenSearch) and advanced prompt-engineering.
• Expert in architecting and deploying multi-agent GenAI ecosystems—designing role-based agent hierarchies with shared context stores, dynamic tool selection, feedback loops, and robust guard-rails for complex workflows.
• Strong MLOps & DevSecOps expertise: CI/CD for ML (GitHub Actions/Jenkins), container orchestration (Docker & Kubernetes/EKS), model versioning and monitoring (MLflow, SageMaker Model Monitor).
• Demonstrated commitment to Responsible & Compliant AI—bias detection, privacy-preserving ML, model governance.
• Hands-on with cloud-native GenAI services (AWS SageMaker & Bedrock, Azure OpenAI) and large-scale data platforms (Databricks, Snowflake, PySpark).
• Domain depth in banking & financial-services use-cases (fraud detection, credit-risk modelling, personalised customer engagement) and the ability to translate GenAI capabilities into measurable business value.
• Track record of leading cross-functional squads and mentoring senior data-science talent, shaping technical roadmaps and innovation strategy.
• Published research, patents, or open-source contributions that showcase thought leadership in Generative AI/NLP.

• Proven delivery of retrieval-augmented-generation (RAG) pipelines, including vector-database design (FAISS, Pinecone, Amazon OpenSearch) and advanced prompt-engineering.
• Expert in architecting and deploying multi-agent GenAI ecosystems—designing role-based agent hierarchies with shared context stores, dynamic tool selection, feedback loops, and robust guard-rails for complex workflows.
• Strong MLOps & DevSecOps expertise: CI/CD for ML (GitHub Actions/Jenkins), container orchestration (Docker & Kubernetes/EKS), model versioning and monitoring (MLflow, SageMaker Model Monitor).
• Demonstrated commitment to Responsible & Compliant AI—bias detection, privacy-preserving ML, model governance.
• Hands-on with cloud-native GenAI services (AWS SageMaker & Bedrock, Azure OpenAI) and large-scale data platforms (Databricks, Snowflake, PySpark).
• Domain depth in banking & financial-services use-cases (fraud detection, credit-risk modelling, personalised customer engagement) and the ability to translate GenAI capabilities into measurable business value.
• Track record of leading cross-functional squads and mentoring senior data-science talent, shaping technical roadmaps and innovation strategy.
• Published research, patents, or open-source contributions that showcase thought leadership in Generative AI/NLP