# WSP Hiring AI Engineer 2 to 3 Years | Bengaluru Mumbai Source: https://latestindiajobs.com/wsp-hiring-ai-engineer-bengaluru-mumbai-noida Published: 2026-11-13 | Updated: 2026-11-13 Category: IT Jobs - Company: WSP - Role: AI Engineer, T&I - Location: Bengaluru, Mumbai, Noida - Qualification: Bachelor's in Computer Science, Engineering, Data Science or related field - Experience: 2-3 years (internships or strong projects count) - Apply: https://emit.fa.ca3.oraclecloud.com/hcmUI/CandidateExperience/en/sites/CX_2001/job/98032 WSP is hiring an AI Engineer for its Technology and Innovation (T&I) team to build generative AI solutions for its Middle East portfolio from the Bengaluru GCC. The posting asks for 2 to 3 years of relevant experience, and it says internships or strong project experience count. Locations listed are Bengaluru, Mumbai and Noida. ## Job Overview | Detail | Information | |---|---| | Company | WSP | | Role | AI Engineer, T&I | | Job identification | 98032 | | Posting date | 23 October 2026 | | Locations | Bengaluru (Karnataka), Mumbai (Maharashtra), Noida (Uttar Pradesh) | | Experience | 2 to 3 years, including internships or strong project work | | Education | Bachelor's in Computer Science, Engineering, Data Science or a related field | | Background check | Required (third-party BGV) | | Salary | Not stated in the posting | | Last date | Not stated in the posting | This is not a pure fresher job. If you have solid internships and real AI projects, read on. If you are a first-year graduate with no projects, look at entry-level roles first. ## What the role is about WSP is a large professional services and engineering consulting firm. This role builds production-oriented generative AI solutions: AI products, agents, automation workflows and enterprise integrations, with stress on secure engineering, measurable quality and responsible AI. ## Key Responsibilities - Develop GenAI applications, agents and automation workflows using approved designs and engineering standards. - Implement retrieval-augmented generation (RAG), function calling, structured outputs and prompt patterns to make solutions reliable. - Integrate AI components with enterprise systems, APIs, approved vector search services and governed data sources. - Build and maintain ingestion, chunking, embedding, indexing and retrieval pipelines for structured and unstructured content. - Create evaluation datasets and run repeatable tests for accuracy, grounding, safety, latency and quality. - Apply responsible AI controls such as validation, guardrails, content filtering, traceability and human oversight. - Write clean, reusable, documented Python components and take part in code reviews. - Support deployment, monitoring, incident resolution and continuous optimisation. ## Eligibility Criteria - Bachelor's degree in Computer Science, Engineering, Data Science or a related discipline. - 2 to 3 years of software, data or AI engineering experience, which can include internships or strong project experience. - Successful completion of a background verification check. ## Skills Required **Core:** Python, REST APIs, SQL and software engineering basics; practical understanding of LLMs, embeddings, RAG, prompt engineering and evaluation methods; exposure to Azure AI services, Git, CI/CD and cloud security; strong analytical, troubleshooting, documentation and communication skills. **Stack named in the posting:** Microsoft Azure with Azure OpenAI, Azure AI Search, Azure AI Document Intelligence and Azure Machine Learning; Azure App Service, Functions and Kubernetes where approved; Azure Storage, Key Vault and SQL; Azure DevOps, Git, CI/CD, automated testing and Azure Monitor; REST APIs, microservices, event-driven and serverless patterns. **Preferred:** Azure or Azure AI certification, experience with agentic AI orchestration, document intelligence or enterprise search, MLOps knowledge, and work in agile or globally distributed teams. ## Salary and Benefits The posting does not state salary. For AI roles with 2 to 3 years of experience, pay varies widely by company, so ask the recruiter about the range early. Do not rely on salary figures from third-party sites. ## Is this job right for you? **It suits you if:** - You have built a RAG app, an agent or a document-search project and can explain design choices. - You like testing and evaluation, not only prototypes. The posting stresses repeatable tests for accuracy and safety. - You are comfortable working with stakeholders in the Middle East and across distributed teams. **You may want to skip it if:** - You have no hands-on LLM project yet. - You want a research role. This is applied engineering. - You dislike Azure-heavy stacks, since the posting says equivalent tools are allowed only through formal approval. ## A day in the life This is our reading of the duties. You might start with a stand-up with the product owner, then improve a retrieval pipeline that returns poor answers on long documents. After lunch you write evaluation cases, run them and log accuracy and latency. Later you review a teammate's Python code, check a guardrail rule, and look at a monitoring dashboard for a deployed assistant. Documentation and clear reporting to distributed teams are part of the work. ## 7-day preparation plan (general guidance) 1. **Day 1:** Revise Python fundamentals and write a small REST API. 2. **Day 2:** Re-read how embeddings and vector search work. Rebuild a tiny RAG pipeline. 3. **Day 3:** Study chunking strategies and how they change answer quality. 4. **Day 4:** Learn how to evaluate an LLM app: ground-truth sets, groundedness, latency, cost. 5. **Day 5:** Skim Azure OpenAI and Azure AI Search documentation. 6. **Day 6:** Read about responsible AI: guardrails, content filters, human oversight. 7. **Day 7:** Prepare a 5 minute walkthrough of your best project and do a mock interview. ## Likely interview questions (general guidance) - How does RAG reduce hallucination? *Hint:* it grounds the model in retrieved documents, but retrieval quality still decides accuracy. - How would you evaluate a chatbot before release? *Hint:* a test set, groundedness checks, safety tests, latency and cost. - What is function calling? *Hint:* the model returns a structured request that your code executes. - How do you secure an AI app that uses company data? *Hint:* access control, key management, data governance, logging. - Tell us about a bug you found in your own AI project. ## Career path and what to learn next From this role you can grow into senior AI engineer, MLOps engineer, solutions architect or AI product roles. Useful next steps: an Azure AI certification, MLOps practices, agent orchestration frameworks and cloud security. Compared with a generic software developer role, this path asks for deeper knowledge of evaluation and governance. ## Application checklist - Rewrite your resume to lead with AI projects and measurable results. See [resume tips for freshers](https://latestindiajobs.com/resume-tips-for-freshers-india). - Add GitHub links with a readable README for each project. - Keep documents ready for the background check. See [documents required for job applications](https://latestindiajobs.com/documents-required-for-job-applications-india). - Make sure dates and employers on your resume match your documents exactly, because the posting stresses accurate information for BGV. ## Scam and safety reminders Apply only on WSP's own career site. A real recruiter will not ask for money. See [how to identify fake job offers](https://latestindiajobs.com/how-to-identify-fake-job-offers-india). ## Common mistakes in AI engineer applications These are general observations and not WSP's official guidance. - **Showing only a chatbot demo.** A single notebook that calls a model API is common. This posting talks about evaluation datasets, guardrails, monitoring and deployment, so show how you tested and shipped something, even a small app on a free cloud tier. - **Not measuring quality.** If your project has no accuracy or groundedness numbers, add a small test set of 20 to 30 questions and report results. It takes a day and changes how your work reads. - **Ignoring security and governance.** The posting repeats words like secure, governed, approved and responsible. Be ready to talk about secrets management, data access and what you would not send to a model. - **Overstating experience.** The role asks for 2 to 3 years including internships or strong projects. Be precise about what you built alone and what your team did, because the background verification process also checks what you claim. - **Skipping Azure basics.** You do not need every service, but you should know what Azure OpenAI and Azure AI Search do and how they fit together in a RAG design. A good way to prepare is to rebuild one project end to end: ingest documents, chunk and embed them, store them in a search index, answer questions, and write a short evaluation report. Walk through that story in the interview. ## How to Apply Open the official WSP careers page for job 98032, choose your preferred location and apply with an updated resume. Details can change, so confirm on the page. ## Related Reading - [Off campus drives guide for India](https://latestindiajobs.com/off-campus-drives-guide-india) - [Accenture AI and ML associate in Bengaluru](https://latestindiajobs.com/accenture-hiring-ai-ml-computational-science-associate-bengaluru) - [Cisco hiring Software Engineer in Bangalore](https://latestindiajobs.com/cisco-hiring-software-engineer-bangalore) - Browse more at /category/it-jobs ## FAQ **Can freshers apply for the WSP AI Engineer job?** The posting asks for 2 to 3 years of experience, though internships or strong project experience count. **Which locations are listed?** Bengaluru, Mumbai and Noida. **Which skills matter most?** Python, REST APIs, SQL, LLMs, embeddings, RAG, prompt engineering, evaluation and Azure AI services. **Is there a background check?** Yes. Employment is subject to a third-party background verification. **Is the salary mentioned?** No, WSP does not state salary in the posting.