# Senior AI Engineer

**Company**: WSP
**Location**: Bengaluru, Karnataka
**Experience**: senior
**Job type**: full-time
**Category**: Engineering
**Industry**: Consulting

**Apply**: https://emit.fa.ca3.oraclecloud.com/hcmUI/CandidateExperience/en/sites/CX_2001/job/94762?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_da7b0c89-e5e

## Description

We are seeking a Senior AI Engineer with experience shipping AI products, not just prototyping them. The ideal candidate will have a strong background in both core ML and modern GenAI.

The Senior AI Engineer will work at the intersection of engineering rigor and product ownership, building, deploying, and operating ML and LLM-powered applications on Azure.

Responsibilities:

- Own end-to-end deployment of AI applications on Azure, from classical ML models to production release, monitoring, and iteration.

- Build and evaluate core ML models where GenAI isn't suitable.

- Design and implement guardrails, such as content filtering and prompt injection defense.

- Build and maintain MLOps/LLMOps pipelines, including CI/CD for models and prompts.

- Manage the model lifecycle, including model selection and routing.

- Implement observability, logging, tracing, and alerting for LLM applications.

- Architect RAG and agentic systems, including vector store design and retrieval tuning.

- Collaborate cross-functionally with product owners, architects, and business stakeholders.

- Contribute to AI governance, supporting responsible AI reviews and model risk assessments.

Qualifications:

- Minimum 3-4 years of hands-on software/ML engineering experience, with at least 2 years on GenAI/LLM applications.

- Solid grounding in core AI/ML fundamentals.

- Hands-on experience with standard ML libraries (scikit-learn, XGBoost/LightGBM, pandas, NumPy) and at least one deep learning framework (PyTorch or TensorFlow).

- Strong working knowledge of the Azure AI/ML stack.

- Practical experience with MLOps/LLMOps tooling.

- Demonstrated experience building guardrails and safety layers in production.

- Solid Python engineering skills.

- Experience with at least one orchestration framework (LangGraph, Semantic Kernel, LangChain).

- Understanding of RAG architecture.

- Comfort with monitoring/observability tooling (Application Insights).

What Sets Strong Candidates Apart:

- Deployment maturity across both classical ML and GenAI.

- Experience redesigning a guardrail after it failed in production.

- Tracking and controlling LLM cost at scale.

- Approach to versioning and rolling back a prompt or model change.

- Measuring and reducing hallucination or drift in a live system.

- Choosing between classical ML and LLM models.

## Skills

### Required
- Python
- scikit-learn
- XGBoost
- LightGBM
- pandas
- NumPy
- PyTorch
- TensorFlow
- Azure AI/ML
- MLOps
- LLMOps
- LangGraph
- Semantic Kernel
- LangChain
- Application Insights

### Nice to have
- Copilot Studio
- Power Platform
- voice-based AI
- multimodal AI
- enterprise AI governance

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Source: [Apply at emit.fa.ca3.oraclecloud.com](https://emit.fa.ca3.oraclecloud.com/hcmUI/CandidateExperience/en/sites/CX_2001/job/94762?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)