Job Title : Principle Machine Learning Engineer / Lead Architect
Experience : 5+ years
location : Bangalore
Role Summary :
The Principal ML Engineer serves as the lead architect for the organization's machine learning infrastructure. This individual is responsible for the design, implementation, and scaling of high-performance ML systems that transition research into reliable production services.
AI Foundation Layer
- AI Hub Development: Architecting a centralized hub for cross-team discovery, experimentation, and standardized deployment of ML assets.
- Model Registry Management: Designing a sophisticated registry to track model lineage, versioning, and lifecycle states (Development, Staging, Production).
- Observability & Monitoring: Building automated frameworks for real-time monitoring of data drift, model performance decay, and system health.
Key Responsibilities
- Infrastructure Strategy: Defining the technical roadmap for MLOps, ensuring the stack remains modular, scalable, and cost-effective.
- Automation: Developing CI/CD/CT (Continuous Training) pipelines to minimize manual intervention in the model deployment lifecycle.
- Technical Leadership: Providing mentorship to engineering teams and conducting high-level code reviews to maintain rigorous engineering standards.
- Systems Integration: Partnering with data and backend teams to ensure seamless integration between data lakes and inference services.
Required Skill Set
- Machine Learning: Expert proficiency in PyTorch, Tensor Flow, or JAX.
- MLOps Platforms: Extensive experience with Kabbelow, MLflow, Amazon SageMaker, or Google Vertex AI.
- Containerization & Orchestration: Mastery of Kubernetes (K8s) and Docker within cloud environments (AWS/GCP/Azure).
- Software Engineering: Advanced programming skills in Python, with a focus on high-concurrency backend services.
- Data Engineering: Proficiency with orchestration tools such as Airflow or Dagster.
- Monitoring Tools: Hands-on experience with Prometheus, Grafana, Arize, or WhyLabs
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