Deutsche Bank—AI & Agentic Systems Engineering, VP

Job description

Description: 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 complex technical concepts clearly to technical and non-technical audiences. Your Skills & Experience: - Extensive hands-on software engineering experience, including the design and delivery of enterprise-scale distributed applications and production-grade AI or Generative AI solutions. - Deep expertise in ReactJS and TypeScript, combined with strong Python or Java engineering skills and practical experience with API, microservices and asynchronous application design; experience with FastAPI, Spring Boot or comparable frameworks is beneficial. - Strong experience with BigQuery and PostgreSQL, cloud-native architectures, Git-based development, CI/CD, Docker, Kubernetes, infrastructure as code, observability and automated testing; practical knowledge of GitHub or Azure DevOps, Terraform and OpenTelemetry is beneficial. - Practical expertise in LLM applications, AI agents, RAG architectures, vector databases, semantic or hybrid search, model evaluation and LLMOps, with hands-on experience in relevant orchestration, evaluation and observability tools. - Solid grounding in machine learning, feature engineering, model explainability and analytical modelling, using relevant Python ML libraries. - Strong understanding of financial markets and investment products is highly desirable, with experience in asset management, ETFs or mutual funds and knowledge of key investment performance and risk metrics, including Sharpe Ratio, Information Ratio, Sortino Ratio, Alpha, Beta and Tracking Error. - Strong engineering mindset and ownership mentality, with sound judgement and the ability to structure ambiguity, solve complex problems, make pragmatic technical decisions and balance delivery speed, quality, risk, maintainability and long-term scalability. - Excellent stakeholder management, communication and collaboration skills, with the ability to understand business needs, translate them into clear technical choices, manage expectations, influence decisions and build trusted relationships across business, product, technology and control functions. - Demonstrated technical leadership, including the ability to set direction, lead by example, mentor and develop engineers, provide constructive challenge, drive accountability and foster an inclusive culture of learning, experimentation and engineering excellence. - Ability to lead delivery across the engineering lifecycle, establish fit-for-purpose standards and working practices, manage dependencies and trade-offs, and maintain focus on measurable business and user outcomes. - Bachelor’s degree in Computer Science, Engineering, Science or a related discipline, or equivalent professional experience. We at DWS are committed to creating a diverse and inclusive workplace, one that embraces dialogue and diverse views, and treats everyone fairly to drive a high-performance culture. The value we create for our clients and investors is based on our ability to bring together various perspectives from all over the world and from different backgrounds. It is our experience that teams perform better and deliver improved outcomes when they are able to incorporate a wide range of perspectives. We call this #ConnectingTheDots.