Job Description: Engineer is responsible for managing or performing work across multiple areas of the bank's overall IT Platform/Infrastructure including analysis, development, and administration. It may also involve taking functional oversight of engineering delivery for specific departments. Work includes: •Planning and developing entire engineering solutions to accomplish business goals •Building reliability and resiliency into solutions with appropriate testing and reviewing throughout the delivery lifecycle •Ensuring maintainability and reusability of engineering solutions •Ensuring solutions are well architected and can be integrated successfully into the end-to-end business process flow •Reviewing engineering plans and quality to drive re-use and improve engineering capability •Participating in industry forums to drive adoption of innovative technologies, tools and solutions in the Bank. About DWS At DWS, we’re capturing the opportunities of tomorrow. You can be part of a leading, client-committed, global Asset Manager, making an impact on individuals, communities, and the world. Join us on our journey, and you can shape our transformation by working side by side with industry thought-leaders and gaining new and diverse perspectives. You can share ideas and be yourself, whilst driving innovative and sustainable solutions that influence markets and behaviours for the better. Every day brings the opportunity to discover a new now, and here at DWS, you’ll be supported as you overcome challenges and reach your ambitions. This is your chance to lead an extraordinary career and invest in your future. Read more about DWS and who we are here. Our Team Chief Operating Office (COO) Customer Success Management (CSM) enables the successful delivery and adoption of strategic themes across DWS. The function acts as the link between business, technology, operations and enabling functions, ensuring that ideas translate into measurable business value. Through stakeholder engagement, governance, and execution support, COO CSM accelerates transformation, innovation, and operational excellence for DWS. COO CSM is a core pillar of DWS' target operating model, bringing together business, product, engineering and enabling functions to accelerate the path from opportunity identification to scalable business outcomes. The India-based engineering team is our technology innovation and delivery hub supporting rapid prototyping, research and validation of emerging AI and modern technology solutions. Working closely with business, product and technology stakeholders, the team turns early-stage ideas into reusable, scalable solutions that can progress from proof of concept to enterprise adoption. Your Key Responsibilities AI & Agentic Systems Engineering • Architect and deliver enterprise-grade AI solutions using Generative AI, Large Language Models and agentic frameworks, including AI copilots, domain-specific assistants and multi-agent workflows. Apply tools such as LangGraph, LangChain, Semantic Kernel or equivalent frameworks where suitable. • Design planning, orchestration, reviewer, evaluator and execution agents, with appropriate human-in-the-loop controls, safety mechanisms and monitoring. • Translate prioritized business and investment challenges into scalable technical architectures and production-ready AI services. Generative AI, RAG & AI Platform Engineering • Develop LLM-powered applications using leading commercial and open-source models and platforms, such as Azure OpenAI, Google Vertex AI, Hugging Face or equivalent, applying prompt engineering, structured outputs, reasoning frameworks and autonomous workflows. • Design retrieval-augmented generation solutions using semantic and hybrid search, embeddings, enterprise knowledge bases, metadata enrichment, reranking and retrieval-quality optimization. Use vector databases, PostgreSQL with pgvector, Azure AI Search or equivalent services where beneficial. • Establish LLMOps capabilities covering prompt lifecycle management, model evaluation, observability, performance and cost monitoring, testing, validation and responsible AI controls. Apply tools such as MLflow, LangSmith, OpenTelemetry or equivalent platforms where appropriate. Full-Stack, Data & Cloud Engineering • Lead hands-on development of modern React and TypeScript front ends and Python- or Java-based back-end services, APIs and microservices, using frameworks such as FastAPI, Spring Boot or equivalent. • Build reliable data and analytics solutions using BigQuery and PostgreSQL, supported by cloud-native, event-driven and distributed architectures. • Embed engineering excellence through automated testing, CI/CD, infrastructure as code, containerization, observability, monitoring, logging and site reliability practices, using Git, GitHub or Azure DevOps, Docker, Kubernetes, Terraform and relevant testing frameworks. Machine Learning & Financial Analytics • Design predictive and analytical models, including classification, ranking, recommendation and forecasting solutions, using appropriate supervised and unsupervised learning methods and established Python ML libraries such as scikit-learn, PyTorch or equivalent. • Apply feature engineering, model explainability and robust validation to deliver transparent, decision-relevant analytics, using tools such as SHAP or equivalent where appropriate. • Bring practical understanding of asset management, investment products and performance and risk measures to the design of relevant solutions. Technical Leadership & Collaboration • Define technical strategy, architecture and reusable engineering patterns for AI-powered products and platforms, while guiding prototypes from proof of concept to enterprise deployment. • Partner with product managers, business stakeholders, use case owners and control functions to align priorities, manage trade-offs and deliver measurable outcomes. • Mentor engineers, strengthen engineering standards and communicate compl