Intern- MLOps (QC08910)
AEREO (earlier known as Aarav Unmanned Systems) is India’s leading drone solution provider in the commercial segment. We provide end to end solutions to government and private enterprises in the field of mining & metals, urban planning, large infrastructure, irrigation, agriculture and energy. We are early starters and market leaders in the Indian drone industry. Our belief is to solve real problems and use drone technology as a revolution. Our strength is our perseverance, clarity, collaborative approach, innovation and our team.
We are funded by some of the well-known Indian VCs in our growth journey so far. However, our business is already self-sustaining and growing at a fast pace. We love machines, especially aerial robots and believe that drones are shaping the future of the world. Aereo is actively looking for self-driven and process-oriented individuals who would be interested in joining team Aereo in this fascinating growth journey and be an early contributor to the drone ecosystem of the country which is growing at a very exciting and fast pace.
At the heart of our software ecosystem is Aereo Cloud, our geospatial intelligence platform that enables customers to visualize, analyze, and derive insights from large-scale drone data.
About the Role:
The Data Science team is looking for an ML Ops Intern to support the integration of AI models into scalable, production-grade systems.
Rather than focusing on model research, this role is centered around developing infrastructure, tooling, and deployment workflows that enable AI models to be trained, versioned, evaluated, and deployed at scale. You will work closely with Machine Learning Engineers on real production systems and gain hands-on experience in building robust ML platforms.
Throughout the internship, you will be mentored closely on production-grade ML engineering practices with the goal of becoming capable of independently owning end-to-end MLOps projects.
Key Responsibilities:
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Build and improve ML training and inference pipelines.
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Develop reusable tooling for model training, evaluation, and deployment.
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Automate workflows using CI/CD pipelines.
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Improve infrastructure around model versioning, dataset management, and experiment tracking.
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Optimize inference pipelines for performance, scalability, and cost.
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Build proof-of-concepts around modern ML infrastructure and deployment techniques.
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Work with Docker, GPU workloads, cloud infrastructure, and production systems.
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Collaborate with Machine Learning Engineers to productionize computer vision models.
Requirements:
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Strong programming skills in Python.
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Good understanding of software engineering fundamentals and clean coding practices.
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Familiarity with Git and version control.
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Basic understanding of Machine Learning concepts.
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Comfortable working with Linux environments.
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Strong problem-solving ability and willingness to learn unfamiliar technologies.
Tech Stack
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Python
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PyTorch
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Docker
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Git
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CUDA
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Cloud Provider (AWS, Azure, GCP)
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GitLab CI/CD
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MLOps tooling (MLflow, DVC, Weights & Biases, FiftyOne, etc.)
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Geospatial Libraries
Preferred Backgrounds:
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Exposure to domains like computer vision, NLP, or time-series analysis is welcome, but not mandatory.
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Students or early professionals looking to gain hands-on experience in ML Ops are encouraged to apply.
