AI Engineer
NTT DATA BUSINESS SOLUTIONS · Bengaluru · onsite · Posted 61d ago
Undisclosed2–5 yrsfulltimeSourced from
About a minute. You'll make an account on the way through.
About this role
Req ID: phone_number
We are currently seeking a AI Engineering to join our team in Bangalore, Karnataka, India.
Job Summary 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.
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.
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.
- 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
- 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.
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
- 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.
Sourced from naukri
Skills
AutomationAgileComponent Designcloud platformsapisautomated testingamazon web servicesai engineerBrandingAgile DeliveryClient SuccessAnalyticsClean CodeBankingcloudcomplianceAi PlatformAi SolutionsMicrosoft AzureAPI IntegrationArtificial Intelligence
About NTT DATA Business Solutions
NTT DATA BUSINESS SOLUTIONS
IT Services & Consulting
4k+
employees
1989
37 years old
Hyderabad / Secunderabad, Telangana
India
About
NTT DATA Business Solutions helps clients innovate and transform through expert SAP solutions, advisory, implementation, and managed services.
Industries
IT Services & Consulting
Employee ratings
2,105 reviews
4.1/ 5
Culture
4.0
Career growth
3.5
Work-life
4.0
Employees rate it well for
Work Life Balance176Salary146Senior Management116Work Culture99Work Environment83
Find them on