Description
WSP is seeking an AI Engineer to develop and support production-oriented Generative AI solutions for the Middle East AI portfolio from the Bengaluru GCC.
The role will work across AI products, agents, automation workflows and enterprise integrations, with emphasis on secure engineering, measurable quality and responsible AI.
Responsibilities
- Develop GenAI applications, agents and automation workflows using approved designs and engineering standards.
- Implement retrieval-augmented generation, function calling, structured outputs and prompt patterns to improve solution reliability and usability.
- 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 execute repeatable testing for accuracy, grounding, safety, latency and solution quality.
- Apply responsible AI controls including validation, guardrails, content filtering, traceability and human oversight.
- Develop clean, reusable and well-documented Python components and participate in code reviews.
- Support deployment, monitoring, incident resolution and continuous optimisation of AI solutions.
Qualifications
- Bachelor’s degree in Computer Science, Engineering, Data Science or a related discipline.
- 0–3 years of relevant software, data or AI engineering experience, including internships or strong project experience.
- Working knowledge of Python, REST APIs, SQL and software engineering fundamentals.
- Practical understanding of LLMs, embeddings, RAG, prompt engineering and evaluation methods.
- Exposure to Azure AI services, Git-based development, CI/CD and cloud security principles.
- Strong analytical, troubleshooting, documentation and stakeholder communication skills.
WSP-Aligned Technology Stack
The role will use and support the approved enterprise stack below. Equivalent technologies may be considered only where formally approved through WSP architecture, security and governance processes.
- Cloud platform: Microsoft Azure
- AI services: Azure OpenAI, Azure AI Search, Azure AI Document Intelligence, Azure Machine Learning and approved Azure AI services
- Application hosting: Azure App Service, Azure Functions and Azure Kubernetes Service where architecturally approved
- Data and security: Azure Storage, Azure Key Vault, SQL and approved NoSQL services
- Engineering lifecycle: Azure DevOps, Git, CI/CD, automated testing, Azure Monitor and approved observability services
- Architecture: REST APIs, microservices, event-driven and serverless patterns, secure API-first integration
Preferred Qualifications
- Microsoft Azure or Azure AI certification.
- Experience with agentic AI orchestration, document intelligence or enterprise search.
- Knowledge of MLOps, model lifecycle management and production monitoring.
- Experience working in multi-disciplinary, agile or globally distributed teams.