WSP Hiring AI Engineer 2 to 3 Years | Bengaluru Mumbai

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

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.

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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.
• Add GitHub links with a readable README for each project.
• Keep documents ready for the background check. See documents required for job applications.
• 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.

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.

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