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Tier-1 brand, popular ML title, mid-level experience requirement, and Bangalore location increase candidate density.
Core ML engineering skills are transferable, but aerospace domain knowledge and specific tools raise sensitivity.
Explicit 3+ years requirement, mandatory ML/CV stack, and aerospace domain increases filtering stringency.
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Own the end-to-end lifecycle of ML models including development, optimization, deployment, and monitoring in production.
Architect and implement scalable ML pipelines (MLOps) to automate training, testing, and deployment of AI models.
Collaborate with data scientists and product managers to integrate AI/ML capabilities into engineering and digital products, focusing on computer vision, NLP, and LLM technologies.
Minimum 3+ years professional experience as an ML Engineer, Data Scientist, or Software Engineer focused on AI.
Education: BTech/MTech/PhD in Computer Science, Data Science, Mathematics, or related quantitative field (or equivalent practical experience).
Strong programming skills in Python and/or Go with ability to deliver clean, production-quality code.
Proficiency with ML frameworks such as PyTorch, TensorFlow/Keras and experience with computer vision techniques and data engineering/orchestration tools (e.g., MLFlow, DVC, Spark).
Experienced in building and deploying scalable AI/ML systems including MLOps pipelines and model fine-tuning for performance in cloud or edge deployments.
Practitioner with domain knowledge in computer vision (object detection, image segmentation) and familiarity with large language models and knowledge graphs.
Able to work in a cross-functional agile environment, bridging data science and software engineering to deliver impactful digital engineering solutions.