GDS Consulting_ARTIFICIAL INTELLIGENCE AND AUTOMATION Senior Data Scientist (Gen AI)

Job Title: EY - GDS Consulting - AIA - Gen AI - Senior At EY, we’re all in to shape your future with confidence. We’ll help you succeed in a globally connected powerhouse of diverse teams and take your career wherever you want it to go. Join EY and help to build a better working world. AI Developer (Python) Experience – 4-8 years’ experience   Skills and attributes for success Candidate must possess proficiency in the following technologies: Core AI Engineering & LLM Frameworks: Python, LangChain, LangGraph, Auto Gen, Google Agent SDK, Model Context Protocol, Skills based agent frameworks. GenAI / Agentic AI Systems: LLM application development, agentic AI architectures, multi-agent workflows, prompt engineering, AI system design, tool/function calling, enterprise AI integration. RAG, Embeddings & Vector Search: Retrieval-Augmented Generation pipelines, embeddings, semantic search, context retrieval strategies, Azure AI Search, Pinecone, FAISS, Redis Vector, pgvector. Backend & API Engineering: Python, Fast API, REST API design, scalable API platforms, event-driven systems, microservices architecture, third-party system integration. Cloud, Infrastructure & Containers: Azure OpenAI, Azure AI Services, Docker, Kubernetes, OpenShift, cloud-native application development, containerized deployments. Databases & Data Platforms: SQL, NoSQL, MongoDB, Redis, ClickHouse, database design, performance optimization, data accuracy and integrity. AI Model Engineering: Model fine-tuning, LoRA, BERT, LLM architecture understanding, evaluation techniques, AI performance monitoring. AI Governance, Security & Responsible AI: PII protection, data privacy, AI security, compliance, responsible AI practices, governance controls, monitoring and observability frameworks. DevOps & Engineering Practices: GitHub Actions, GitLab CI, CI/CD pipelines, source control, automated testing, production deployment practices, observability and monitoring. Preferred Technologies: Rust, Go, Kubernetes, OpenShift, advanced vector database platforms, enterprise-scale LLM deployment patterns. To qualify for the role, you must have Minimum of 4+ years of professional engineering experience, with hands-on experience in backend/platform engineering and GenAI/LLM systems. Bachelor’s degree B.E./B.Tech in Computer Science, IT, or related engineering discipline. Strong hands-on proficiency in Python for AI application development, backend services, and platform engineering. Demonstrable experience building LLM-powered applications, including RAG pipelines, agentic workflows, prompt engineering, and enterprise AI integrations. Hands-on expertise with LangChain and LangGraph, with exposure to frameworks such as AutoGen, Google Agent SDK, Model Context Protocol, or Skills-based agent frameworks. Experience designing and implementing Retrieval-Augmented Generation pipelines using vector databases such as Azure AI Search, Pinecone, FAISS, Redis Vector, or pgvector. Strong understanding of embeddings, semantic search, context retrieval strategies, chunking approaches, ranking, and retrieval optimization. Proficiency in building scalable backend services using Python, FastAPI, REST APIs, microservices, and event-driven architecture. Experience working with Azure OpenAI and Azure AI Services for enterprise-grade AI solution development. Strong understanding of Docker and containerized deployments, with exposure to Kubernetes or OpenShift preferred. Experience implementing CI/CD pipelines using GitHub Actions, GitLab CI, or similar DevOps tooling. Proficiency in SQL and NoSQL database design, query optimization, and scalable data interaction patterns. Familiarity with AI evaluation, observability, monitoring, and production support for LLM-based systems. Strong understanding of PII protection, data privacy, AI security, compliance, and responsible AI practices. Ability to collaborate with cross-functional teams, including AI engineers, data engineers, cloud/platform teams, security teams, product owners, and business stakeholders. Experience in banking or financial services domain is preferred, especially exposure to regulatory, security, and compliance requirements. Ability to troubleshoot and debug issues across AI pipelines, backend services, APIs, vector stores, cloud services, and production environments. Commitment to engineering quality, including maintainable code, automated testing, reusable components, documentation, and scalable design practices. What we looking for Provides intermediate to senior-level system analysis, architecture design, development, and implementation of AI platforms, backend services, APIs, and enterprise AI systems. Designs and develops scalable GenAI applications using LLM frameworks, RAG pipelines, vector databases, and cloud-native backend services. Translates business and technical requirements into robust AI engineering solutions, including APIs, agentic workflows, retrieval pipelines, integrations, and data processing components. Builds, tests, and deploys AI-powered backend services using Python, FastAPI, Azure OpenAI, Azure AI Services, vector databases, and modern DevOps practices. Develops and maintains RAG pipelines, including document ingestion, chunking, embeddings generation, vector indexing, retrieval optimization, and response grounding. Implements agentic AI architectures using frameworks such as LangChain, LangGraph, AutoGen, Google Agent SDK, or Model Context Protocol-based integration patterns. Integrates AI solutions with enterprise systems, third-party applications, APIs, data platforms, and workflow tools. Elevates code into development, test, staging, and production environments following established CI/CD, change control, and release management processes. Provides production support for AI applications, including monitoring, troubleshooting, performance tuning, issue resolution, and root cause analysis. Participates in design reviews, code reviews, testing reviews, and architecture discussions to ensure scalable, secure, and maintainable AI systems. Applies software development methodology and follows architecture, information security, and responsible AI standards. Contributes to AI observability and evaluation practices by monitoring model behavior, retrieval quality, latency, accuracy, hallucination risks, and system performance. Supports implementation of AI governance controls including PII protection, data privacy, access control, compliance, responsible AI guardrails, and auditability. Understands client business functions, technology needs, and enterprise constraints, especially in regulated banking and financial services environments. Contributes to optimizing system performance through efficient API design, database optimization, vector search tuning, caching strategies, and scalable infrastructure patterns. Supports the integration and maintenance of data pipelines between enterprise systems, vector databases, APIs, and backend AI services, ensuring data accuracy and integrity. Maintains and updates technical documentation for AI platforms, backend components, architecture, deployment flows, data pipelines, and operational procedures. Applies intermediate to strong knowledge of backend security, AI security, data protection, and cloud-native engineering best practices during solution development. Qualifications: Bachelor’s degree in computer science, Information Technology, or a related field. EY | Building a better working world EY is building a better working world by creating new value for clients, people, society and the planet, while building trust in capital markets. Enabled by data, AI and advanced technology, EY teams help clients shape the future with confidence and develop answers for the most pressing issues of today and tomorrow. EY teams work across a full spectrum of services in assurance, consulting, tax, strategy and transactions. Fueled by sector insights, a globally connected, multi-disciplinary network and diverse ecosystem partners, EY teams can provide services in more than 150 countries and territories