Key Responsibilities
Build and maintain end-to-end ML pipelines (train → validate → deploy → monitor)
Productionize models for:
Batch scoring (scheduled pipelines)
Real-time APIs (Kubernetes-hosted services)
Standardize deployments using:
Dataiku (Automation + API nodes)
Containerized services on Azure Kubernetes Service (AKS)
Implement CI/CD pipelines (Azure DevOps/GitHub Actions) for ML workflows
Establish monitoring and alerting:
Model performance, drift, failures, latency
Operationalize GenAI systems (LangChain/RAG):
Prompt/version control, evaluation pipelines, tracing, cost controls
Define and enforce model governance:
Model registry, approvals, auditability, documentation
Build reusable templates and paved roads for data scientists

Required Qualification
sBachelor's degree in Computer Science, Engineering, or related field (16 years of formal education)
.5 to 7 years of overall IT experience with 3+ years in Model Op




sStrong Python + software engineering practices (testing, Git, modular code
)Experience deploying ML systems in batch and real-time setting
sHands-on with Docker + Kubernetes (AKS preferred
)Experience with CI/CD pipelines (Azure DevOps or GitHub Actions
)Experience implementing monitoring/alerting for production system

s
Collaboration & Suppo
rtAct as the primary platform contact for Model Op
s.Provide support and participate in incident response and root-cause analysi
s.Mentor junior engineers to enable L1/L2 support and contribute to internal platform standard

s.
Preferred Qualificati
onsDataiku DSS (Automation node, API node, scenari
os)Azure services (ML, Storage, Key Vault, Monit
or)MLflow or model registry experie
nceLangChain / RAG / vector databa
sesObservability tools (Monte Carlo, Langsmith, Datadog or equivalen

ts)

📌 Senior Data Platform Engineer Modelops Bengaluru (India)
🏢 DigiKey Global Capability Center
📍 India