Level

Deutsche Bank

GCP Full-Stack Data & AI Engineer, AS

AI in this role

Full-stack data and AI engineer building cloud-native applications, data pipelines, RAG systems, and agentic workflows on GCP.

geminigcppythonrest-api
ragai-agentsai-safetyfull-stackdata-engineeringconversational-aiagentic-workflowscloud-native

Job Description:

Job Title: GCP Full-Stack Data & AI Engineer

Location: Pune, India


Role Description:

As a GCP Full-Stack Data & AI Engineer, you will deliver secure, scalable cloud-native applications spanning user interfaces, APIs, data services, AI capabilities, deployment, and production support. You will convert business needs into maintainable solutions, contribute to technical decisions, and help improve engineering standards across the software lifecycle.


What we’ll offer you

As part of our flexible scheme, here are just some of the benefits that you’ll enjoy

  • Best in class leave policy.
  • Gender neutral parental leaves
  • 100% reimbursement under childcare assistance benefit (gender neutral)
  • Sponsorship for Industry relevant certifications and education
  • Employee Assistance Program for you and your family members
  • Comprehensive Hospitalization Insurance for you and your dependents
  • Accident and Term life Insurance
  • Complementary Health screening for 35 yrs. and above

Your key responsibilities:
You will contribute to end-to-end delivery across the following areas, taking ownership of assigned solutions while collaborating with technical leads and partner teams:


Full-Stack Application & API Engineering:

  • Translate business and user needs into technical designs, estimates, delivery plans, and production-ready solutions.
  • Develop accessible web interfaces, Python services, and well-documented REST APIs; integrate them with enterprise systems, data stores, event streams, and third-party services.
  • Embed authentication, authorization, validation, error handling, auditability, and automated testing throughout the application lifecycle.

Data & AI Engineering:

  • Build and optimize batch and streaming pipelines and analytical or operational data models on Google Cloud.
  • Integrate RAG, conversational AI, and agentic workflows into enterprise applications, with measurable evaluation for quality, safety, latency, and cost.
  • Enforce data quality, lineage, governance, privacy, access control, and responsible AI requirements.

Infrastructure & Operations:

  • Provision secure, reusable cloud environments and delivery pipelines for frontend, backend, data, and AI components.
  • Own production readiness, deployment, rollback, observability, autoscaling, disaster recovery, runbooks, and support handover.
  • Monitor reliability, performance, security, user experience, and cloud cost; resolve cross-stack incidents through root-cause analysis and corrective action.
  • Contribute to architecture decisions, technical documentation, engineering standards, and knowledge sharing.

Your skills and experience


Mandatory Engineering Skills:

  • Experience: 6–10 years of hands-on software engineering experience delivering enterprise applications and data-intensive systems in production.
  • Core Languages: Advanced proficiency in Python, SQL, and JavaScript or TypeScript, supported by clean-code practices, data structures, object-oriented design, and software design patterns.
  • Frontend: Production experience with React or Angular, including component architecture, state management, routing, responsive design, accessibility, browser security, and performance optimization.
  • Backend & APIs: Strong experience with FastAPI, Flask, and the design, integration, documentation, versioning, and support of REST APIs.
  • Google Cloud & Data: Hands-on experience with Cloud Run, BigQuery, Cloud Storage, and Secret Manager, together with working knowledge of relevant services such as Cloud Composer or Apache Airflow, Pub/Sub, Cloud SQL, Firestore, and Memorystore.
  • DevOps and Quality Engineering: Proficiency with Git, Docker, Terraform, Artifact Registry, Cloud Build, and CI/CD practices, including automated API, integration, UI, security, and performance testing.

Preferred Skills: Experience in one or more of the advanced areas below is advantageous. Candidates are not expected to have production-level expertise in every listed technology.

Advanced Technical & Design Expertise: Candidates should demonstrate practical experience delivering secure, scalable, and supportable solutions across the following areas.

  • Architecture: Design modular solutions spanning user interfaces, services, event-driven components, data stores, AI capabilities, and external integrations, balancing scalability, maintainability, reliability, and cost.
  • Security: Apply OAuth 2.0, OpenID Connect, JWT, IAM, API gateways, CORS, rate limiting, secrets management, input validation, and OWASP-aligned practices.
  • Quality Engineering: Strong hands-on experience with API testing using Postman and automated test frameworks. Ability to implement unit, integration, contract, UI, end-to-end, security, and performance tests using tools such as Pytest.
  • Operations & Reliability: Implement observability, autoscaling, SLOs/SLIs, incident response, capacity planning, and resilience using Google Cloud Operations.
  • Data Engineering: Apply data modelling, batch and streaming design, orchestration, data quality, lineage, governance, query optimization, and secure publishing practices.
  • Generative & Agentic AI: Build and evaluate RAG, conversational, and tool-enabled agent solutions using Gemini models, embeddings, vector databases, Dialogflow CX or CX Agent Studio, and the Google Agent Development Kit (ADK). Familiarity with multi-agent orchestration, agent-to-agent protocols, guardrails, and human oversight is desirable.
  • Technical Leadership: Contribute to design and code reviews, promote reusable standards, support other engineers, and communicate technical trade-offs across product, UX, security, architecture, data, and operations teams.

How we’ll support you

  • Training and development to help you excel in your career
  • Coaching and support from experts in your team
  • A culture of continuous learning to aid progression
  • A range of flexible benefits that you can tailor to suit your needs

About us and our teams

Please visit our company website for further information:

https://www.db.com/company/company.html

We strive for a culture in which we are empowered to excel together every day. This includes acting responsibly, thinking commercially, taking initiative and working collaboratively.

Together we share and celebrate the successes of our people. Together we are Deutsche Bank Group.

We welcome applications from all people and promote a positive, fair and inclusive work environment.

How we rate this

GCP Full-Stack Data & AI Engineer, AS at Deutsche Bank rates 65 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.

Classification

Works on AI. The daily work is on AI products, without building the model.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

RAGAI AgentsAI SafetyFull StackData EngineeringConversational AIAgentic WorkflowsCloud Native

Questions you could be asked

  1. How would you design a retrieval step so the model answers from real data instead of guessing?
  2. How do you decide when an AI agent can act on its own versus asking for approval first?
  3. How do you think about the risk of an AI system in this kind of role failing silently?
  4. Tell me about a project where full stack was part of your work. What did you do?
  5. Tell me about a project where data engineering was part of your work. What did you do?

Adapt your resume

  • List these exact terms on your resume: RAG, AI Agents, AI Safety, Full Stack, and Data Engineering. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
  • Show where AI is part of your daily process, not a one-off project — this role expects it to be a running habit.

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