Back
Tier-1 brand, metro location, and AI role attractiveness increase applicant density.
Technical ML/AI engineering skills transfer across industries but require specialized AI experience, so medium sensitivity.
Multiple strict mandatory technical requirements (Python, AI frameworks, Java, production ML lifecycle) enforce high shortlist filtering.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Develop and own AI-powered Java services and APIs within a distributed environment, ensuring high performance and maintainability.
Hands-on development and productionization of AI components in Python, including support for model training, tuning, inference, and lifecycle management.
Operate and automate AI system deployments with focus on scalability, security, monitoring, and reliability; mentor engineers to uphold engineering and AI best practices.
Proven hands-on Python engineering skills for AI workloads including production code, testing, packaging, and operationalisation.
Experience with modern AI frameworks such as PyTorch, TensorFlow, or Hugging Face.
Strong Java software engineering capabilities covering design, coding, testing, and maintenance of scalable and reliable systems.
Work Experience Required: Not explicitly mentioned in the JD.
Experience bridging software engineering with AI lifecycle operations, including model deployment and automated workflow management.
Comfortable operating in multi-tier distributed systems with a focus on engineering excellence and pragmatic design trade-offs.
Able to troubleshoot complex integrated AI and software issues, providing technical guidance and raising quality standards within engineering teams.