Senior AI Engineer

WSP
Bengaluru, Noida
Workplace: HybridFull timeFunction: Data Science & Machine LearningSkills: ["Collaboration","Stakeholder communication","Production-minded engineering","Problem-solving"]

Own end-to-end deployment of classical ML and GenAI applications on Azure, from model integration to production release, monitoring, and iteration. Build and evaluate ML models where LLMs aren’t the right fit, design guardrails (safety, prompt-injection defenses, PII redaction, validation, human-in-the-loop), and create MLOps/LLMOps CI/CD pipelines with automated evaluation and rollback. Architect RAG/agentic systems, track cost/latency, and support responsible AI reviews.

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FursaFursa
WSP
WSP
2 days ago

Senior AI Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI
Last checked: 3 hours agoStatus: Live

Job Summary

Own end-to-end deployment of classical ML and GenAI applications on Azure, from model integration to production release, monitoring, and iteration. Build and evaluate ML models where LLMs aren’t the right fit, design guardrails (safety, prompt-injection defenses, PII redaction, validation, human-in-the-loop), and create MLOps/LLMOps CI/CD pipelines with automated evaluation and rollback. Architect RAG/agentic systems, track cost/latency, and support responsible AI reviews.
Location: Bengaluru, Noida
Workplace: Hybrid
Employment Type: Full time
Job Function: Data Science & Machine Learning
Seniority: Mid level

Key Responsibilities

  • •Own end-to-end deployment of AI applications on Azure, including production release, monitoring, and iteration for classical ML and Azure OpenAI integrations.
  • •Build and evaluate core ML models when GenAI isn’t suitable, covering classification, regression, forecasting, clustering, and recommendation using traditional ML.
  • •Design and implement production guardrails such as content filtering, prompt-injection defenses, PII redaction, output validation, and human-in-the-loop checkpoints.
  • •Build and maintain MLOps/LLMOps pipelines (CI/CD for models and prompts, feature/data pipelines, automated evaluation harnesses, versioning, and rollback).
  • •Architect RAG/agentic systems and implement observability (logging/tracing/alerting) while tracking accuracy, cost, latency, safety, drift, and uptime.

Key Requirements

  • •Minimum 3–4 years hands-on software/ML engineering, including at least 2 years on GenAI/LLM applications taken to production.
  • •Strong AI/ML fundamentals (supervised/unsupervised learning, model evaluation, feature engineering, class imbalance/overfitting) and knowing when classical ML beats LLMs.
  • •Hands-on experience with standard ML libraries (scikit-learn, XGBoost/LightGBM, pandas, NumPy) and at least one deep learning framework (PyTorch or TensorFlow).
  • •Practical experience with Azure AI/ML stack including Azure Machine Learning, Azure OpenAI Service, Azure AI Foundry, Azure AI Search, and Azure App Service/Functions for deployment.
  • •Hands-on MLOps/LLMOps tooling (CI/CD, Docker, model/prompt versioning, experiment tracking, automated evaluation/testing) and production guardrails for safety/security.
Experience:GenAILLMMLOps/LLMOpsAzure AIEnterprise AI
Skills:CollaborationStakeholder communicationProduction-minded engineeringProblem-solving
Tech Stack:AzureAzure Machine LearningAzure OpenAI ServiceAzure AI FoundryAzure AI SearchAzure App ServiceAzure FunctionsScikit-learnXGBoostLightGBMPandasNumPyPyTorchTensorFlowDockerCI/CDModel versioningPrompt versioningExperiment trackingAutomated testing

Company Brief

WSP
Global professional services firm providing engineering, consulting, and technical services across infrastructure, environment, buildings, transportation, and energy sectors for public- and private-sector clients worldwide.
Industry: Professional Services
Company Size: Enterprise (1,001+ employees)
Revenue: USD 1B+
Growth: Public Company
Valuation: Public Company (Market Cap in USD)
Funding: IPO / Publicly Listed
Headquarters: Montreal, Canada
Founded: 1959
WebsiteLinkedIn