About The ePlane Company

The ePlane Company is at the forefront of India's urban air mobility revolution. Incubated at IIT Madras, we are a deep-tech startup dedicated to designing and building the world's most compact electric flying taxi. Our mission is to make door-to-door flying a reality, drastically reducing commute times and decongesting our cities for a cleaner, greener future. We're a passionate team of engineers, designers, and visionaries working on cutting-edge technology, and we're looking for brilliant minds to help us take flight.

Chart the Course for the Future of Flight

We are looking for a person with a deep understanding of the tooling and lifecycle of ML based systems algorithms including taking them from prototype to production. The selected candidate will own the integrated DevOps and MLOps infrastructure. You will bridge the gap between software engineering, IT infrastructure, and AI-driven model deployment. This also involves work that makes our entire CI/CD pipeline from general software services to safety-critical AI/ML models robust, scalable, compliant, and qualified for regulated engineering processes. This includes instrumenting and hardening live capabilities, and the strategic work of designing a path toward on-premise deployment and formal AI qualification.

Roles and Responsibilities

Build and maintain data pipelines for model training, validation, and continuous retraining
Instrument, monitor, and manage the operational health of the AI/ML capabilities in production including performance, drift, latency, and data quality.
Build CI/CD pipelines, model versioning, rollback procedures, and A/B testing infrastructure
Manage the container orchestration layer and server-side configurations to ensure high availability for internal tools and product operations suites
Lead the feasibility assessment and the implementation of on-premise AI deployment
Own the qualification and governance documentation process across data provenance, model architecture, explainability, and human oversight procedures, robustness testing with applicable regulatory frameworks
Establish and operate a recurring governance review process across all deployed capabilities

Requirements

Required Qualifications

4+ years MLOps or ML infrastructure engineering with production systems experience
CI/CD design and implementation for ML systems (MLflow, DVC, Weights & Biases, or equivalent)
Model monitoring: drift detection, performance degradation alerting, data quality checks
Deep expertise in Kubernetes cluster management, service mesh, and cloud/on-prem hybrid infrastructure
Proven experience in setting up CI/CD pipelines for both non-ML software and ML models
Containerised ML deployment (Docker, Kubernetes)

Preferred Qualifications

Experience deploying ML systems in Safety-critical or regulated domain background where AI output quality must be explainable
Familiarity with change management processes in regulatory environments
RAG system infrastructure experience at scale
Cloud compute job queue management for computationally intensive workloads
On-premise ML/AI deployment experience: quantisation, inference optimisation, GPU cluster management

Erforderliche Kompetenzen

MLOps / ML infrastructure engineering (production systems)CI/CD design and implementation for ML systems (MLflow, DVC, Weights & Biases or equivalent)CI/CD pipelines for non-ML software (software CI/CD)Model monitoring and observability (drift detection, performance degradation alerting, data quality checks)Building and maintaining data pipelines for model training, validation, and continuous retrainingContainerized deployment with DockerKubernetes cluster management, service mesh, and cloud/on-prem hybrid infrastructureHigh-availability server-side configuration and orchestration managementModel versioning, rollback procedures, and A/B testing infrastructureOn-premise ML/AI deployment (quantization, inference optimization)GPU cluster management and cloud compute job queue management for compute-intensive workloadsGovernance, qualification, and regulatory compliance for AI (data provenance, explainability, human oversight, robustness testing, change management)RAG system infrastructure deployment and scaling

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