We're looking for a Sr AI Engineer who has shipped AI products, not just
prototyped them, with equal footing in core ML and up-to-date GenAI. This role sits
at the intersection of engineering rigor and product ownership, you'll build,
deploy, and operate ML and LLM-powered applications on Azure, with real
accountability for what happens after go-live: accuracy, cost, latency, safety,
drift, and uptime.
If you've only worked in notebooks or built demos that never saw production
traffic, this isn't the role. If you've had to explain to a stakeholder why a
model started hallucinating in week three or had to design a rollback plan for a
prompt change, we want to talk to you.
RESPONSIBILITIES
What You'll Do
* Own end-to-end deployment of AI applications on Azure — from classical ML
models and Azure OpenAI Service integrations through to production release,
monitoring, and iteration.
* Build and evaluate core ML models where GenAI isn't the right tool —
classification, regression, forecasting, clustering, or recommendation
problems using traditional ML techniques.
* Design and implement guardrails — content filtering, prompt injection
defence, PII redaction, output validation, and human-in-the-loop checkpoints
for high-risk actions.
* Build and maintain MLOps/LLMOps pipelines — CI/CD for models and prompts,
feature engineering and data pipelines, automated evaluation harnesses,
versioning for models/prompts/embeddings/fine-tunes, and rollback mechanisms.
* Manage the model lifecycle — model selection and routing (classical ML vs.
smaller LLMs vs. frontier models by task complexity and cost), performance
benchmarking, cost-per-call/cost-per-inference tracking, and
deprecation/upgrade planning.
* Implement observability — logging, tracing, and alerting for LLM applications
(token usage, latency, hallucination/error rates, user feedback loops).
* Architect RAG and agentic systems — vector store design, retrieval tuning,
orchestration frameworks (LangGraph, Semantic Kernel, o
📌 Senior AI Engineer (India)
🏢 Wsp
📍 India