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)
🏢 DigiKey Global Capability Center
📍 Bengaluru