Description

Key Responsibilities

- Translate business objectives and propensity-model use cases into a target-state solution architecture and phased implementation roadmap.
- Design AWS architecture for data ingestion, storage, transformation, feature engineering, model training, batch and real-time scoring, and consumption of outputs.
- Define integration patterns, APIs, environment strategy, CI/CD approach, observability, resiliency, and disaster-recovery considerations.
- Establish architectural guardrails for IAM, encryption, networking, data access, auditability, lineage, and responsible model use.
- Guide technology selection and ensure designs meet performance, scalability, reliability, maintainability, and cost objectives.
- Lead design reviews and provide technical direction to Data Scientists, ML Engineers, Data Analysts, and client engineering teams.
- Own architecture documents, solution diagrams, interface specifications, non-functional requirements, and key technical decisions.
- Identify architecture risks, dependencies, assumptions, and mitigations; support issue resolution through build and deployment.

Required Skills and Experience RequirementsAWS architecture: Strong hands-on design experience with S3, Glue, Athena,



Redshift, SageMaker, Lambda, IAM, Lake Formation, CloudWatch, EventBridge, and related AWS services.

Data and ML platforms: Data lake/lakehouse patterns, ETL/ELT, feature engineering, model lifecycle, batch and real-time inference, model registry, and monitoring.

Engineering: Python and SQL awareness, APIs, containerization, Git-based delivery, CI/CD, infrastructure-as-code concepts, and distributed systems.

Architecture practices: Security by design, high availability, scalability, integration architecture, performance engineering, governance, and cost optimization.

Leadership: Robust stakeholder management, workshop facilitation, decision-making, documentation, mentoring, and executive communication.

Preferred Qualifications

- AWS Certified Solutions Architect certification or equivalent practical expertise.
- Experience delivering customer analytics, propensity, churn, cross-sell, upsell, next-best-action, or recommendation solutions.
- Experience in BFSI, insurance, or another regulated industry.
- Exposure to explainable AI, model risk management, and responsible AI controls.

📌 Machine Learning Engineer (Bengaluru)
🏢 IITjobs
📍 Bengaluru