AI Engineering

Req ID: 381097

NTT DATA strives to hire exceptional, innovative and passionate individuals who want to grow with us. If you want to be part of an inclusive, adaptable, and forward-thinking organization, apply now.

We are currently seeking a AI Engineering to join our team in Bangalore, Karnātaka (IN-KA), India (IN).

Senior AI Engineering Professionals - DB Intelligence Programme

Role title
Senior AI Engineer / Senior Full Stack AI Engineer / AI Platform Engineer. Corporate level to be validated: AVP, VP or Director depending on experience.
Programme context
Deutsche Bank is progressing a strategic AI agenda focused on embedding trusted, scalable AI into business-critical banking workflows. The DB Intelligence programme is understood to apply AI to decision intelligence, scenario analysis and risk-aware insight generation, using external developments such as geopolitical, market, regulatory and macroeconomic events alongside internal portfolio and exposure data.
The successful candidates will design, build and industrialise AI-enabled applications that combine modern full stack engineering, data integration, AI/ML services and secure enterprise-grade delivery. Strong hands-on engineering depth across React, Python and Java is essential.
Role purpose
We are seeking senior, hands-on AI engineering professionals who can translate complex business problems into production-grade AI solutions. Candidates will work closely with product owners, data scientists, quants, risk specialists, architects, cyber/security, compliance and business stakeholders to deliver reliable, explainable, secure and observable solutions aligned to Deutsche Bank standards.
Key responsibilities
•    Design and build AI-enabled applications for DB Intelligence, including user-facing workflows, APIs, microservices, orchestration components and data integration layers.
•    Develop modern React / TypeScript front ends with reusable components, strong API integration and user experiences that support explainable AI-assisted workflows.
•    Build Python services for AI/ML integration, model orchestration, RAG, agentic workflows, data processing, evaluation pipelines and automation.
•    Develop and integrate Java / Spring Boot microservices for enterprise backend capabilities, business rules, workflow orchestration and secure service-to-service communication.
•    Work with structured and unstructured data sources, including internal systems, documents, market/event data, portfolio data and enterprise knowledge sources.
•    Contribute to AI architectures using LLM APIs, prompt orchestration, embeddings, vector search, RAG, model evaluation, guardrails and human-in-the-loop review.
•    Engineer solutions that support scenario analysis, event-driven intelligence, impact assessment, portfolio/risk insight generation and decision support.
•    Apply strong engineering discipline: clean code, automated testing, CI/CD, code reviews, observability, performance tuning, resilience and production support readiness.
•    Implement controls for data privacy, entitlement management, audit logging, explainability, traceability, model output monitoring and responsible AI usage.
•    Provide senior technical contribution, design leadership, mentoring and reusable engineering patterns across the programme.
Required experience
•    Significant professional software engineering experience, including recent hands-on delivery of AI, data, analytics or decision-support platforms.
•    Strong React and modern front-end engineering experience, including TypeScript / JavaScript, state management, reusable component design and responsive UI development.
•    Strong Python engineering experience, ideally including FastAPI / Flask, Pandas, data pipelines, AI/ML libraries, LLM integration, model evaluation or automation frameworks.
•    Strong Java engineering experience, preferably with Spring Boot, REST APIs, microservices, event-driven architectures, resilience and enterprise integration patterns.
•    Experience building production-grade applications with clear understanding of security, scalability, availability, latency, observability and maintainability.
•    Practical GenAI / AI application experience, such as LLM APIs, prompt engineering, embeddings, vector databases, RAG, semantic search, agent workflows, hallucination mitigation and guardrails.
•    Experience integrating enterprise data sources and APIs, including SQL databases, document stores, search platforms, messaging/event platforms or data lakes.
•    Strong understanding of secure engineering, authentication/authorisation, entitlement models, data protection and audit requirements.
•    Experience working in Agile delivery teams and communicating complex technical concepts to technical and non-technical stakeholders.
Financial services / banking experience
Financial services experience is highly valuable, particularly in investment banking, corporate banking, risk, markets, research, KYC, credit, portfolio analytics or regulatory technology. Candidates should understand, or quickly adapt to, regulated banking environments with data sensitivity, operational resilience, model risk, access controls, evidence-based decisioning and governance expectations.
Nice to have
•    Cloud platforms such as Google Cloud, AWS or Azure; Kubernetes, Docker, Terraform, Helm and CI/CD tooling such as GitHub Actions, GitLab CI or Jenkins.
•    Vector/search technologies such as pgvector, Elasticsearch/OpenSearch, Vertex AI Search, Pinecone, Weaviate or FAISS.
•    LLM frameworks/orchestration tools such as LangChain, LlamaIndex, Semantic Kernel, Haystack or equivalent.
•    Model evaluation for RAG/GenAI systems, including factuality, grounding, citation accuracy, retrieval precision/recall, latency, toxicity, bias and robustness.
•    Observability tooling such as Prometheus, Grafana, OpenTelemetry, Splunk, ELK or cloud-native monitoring.
•    Responsible AI, model governance, AI risk management, explainability, audit trails and UX patterns for AI-assisted workflows.
Candidate profile
Senior, hands-on engineer with strong ownership, comfort with ambiguity, pragmatic problem solving and the ability to balance innovation with control, resilience and regulatory expectations. Collaborative and credible with senior technology, business, risk and compliance stakeholders.
Indicative technology stack
React, TypeScript, JavaScript; Python, FastAPI / Flask, Pandas, AI/ML and LLM integration libraries; Java, Spring Boot, REST APIs, microservices; SQL / PostgreSQL, vector databases and search platforms; cloud-native platforms, Kubernetes, Docker; CI/CD, automated testing, observability and secure SDLC tooling; LLM APIs, RAG, embeddings, prompt orchestration, model evaluation and guardrails.
Points for Deutsche Bank to validate
•    Confirm programme branding: DB Intelligence, dbIntelligence or alternative internal naming.
•    Confirm target level, location, employment model and working pattern.
•    Confirm preferred cloud/platform stack and any mandatory DB tooling, standards or security requirements.
•    Confirm whether the role should lean full stack AI, AI platform, front-end product, backend Java or Python/ML engineering.
•    Confirm priority banking domain experience, such as risk, markets, portfolio analytics, credit, KYC or research.

About NTT DATA

NTT DATA is a $30 billion business and technology services leader, serving 75% of the Fortune Global 100. We are committed to accelerating client success and positively impacting society through responsible innovation. We are one of the world's leading AI and digital infrastructure providers, with unmatched capabilities in enterprise-scale AI, cloud, security, connectivity, data centers and application services. our consulting and Industry solutions help organizations and society move confidently and sustainably into the digital future. As a Global Top Employer, we have experts in more than 50 countries. We also offer clients access to a robust ecosystem of innovation centers as well as established and start-up partners. NTT DATA is a part of NTT Group, which invests over $3 billion each year in R&D.

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