AI & Agentic Systems Engineering, AVP

Job Title: Engineer, AVP

Location: Pune, India

Corporate Title: AVP

Role Description

• 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.

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

• Assist the architecture and delivery of 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

• Support the development of 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

• Develop 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

• Aid the 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.

Collaboration

• Support the definition of technical strategy, architecture and reusable engineering patterns for AI-powered products and platforms, while enabling the translation of 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.

Your skills and experience

• Hands-on software engineering experience, including the design and delivery of enterprise-scale distributed applications and production-grade AI or Generative AI solutions.
• 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.
• 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

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.