AI Engineering Intern

AEREO (earlier known as Aarav Unmanned Systems) is India’s leading drone solution tech startup 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. The role pertains to the Platform Team in Aereo whose charter is to build and maintain our cloud-based enterprise SaaS platform, Aereo Cloud. Aereo Cloud is a powerful platform which enables organizations to store, manage, visualize and analyze their drone-based geospatial data at PB-scale and generate critical and actionable business insights based on this 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. This role focuses on building robust infrastructure and workflows to bring machine learning features from research to real-world deployment. Internship Duration: 6 months Key Responsibilities: • Collaborate with Data Science and Engineering teams to productionize AI/ML models. • Contribute to proof-of-concepts for new AI-powered features. • Develop and optimize inference pipelines and deployment strategies. • Apply ML Ops best practices in version control, model serving, and monitoring. Requirements: • Proficient in Python with a solid understanding of ML/AI fundamentals. • Strong interest in implementation and systems optimization. • Curious, proactive, and comfortable working across domains. • Bonus: Exposure to cloud services, CI/CD, or scalable ML infrastructure. Preferred Backgrounds: • Exposure to domains like computer vision, NLP, or time-series analysis is welcome, but not mandatory. • Students or early professionals looking to gain hands-on experience in ML Ops are encouraged to apply.