AI Development Engineer
New
P
ProximityAI engineering
IndiaContractSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years of hands-on experience
- Required Skills
- Machine LearningPyTorchTensorflowPrompt EngineeringGenerative AI
Requirements
- Bring strong foundations in machine learning and AI, including linear algebra, probability and statistics, supervised and unsupervised learning, and standard MLP/DNN architectures.
- Understand bias-variance trade-offs, model fitting and selection, hyperparameter tuning, and model performance evaluation.
- Have 5+ years of hands-on experience and working knowledge of Generative AI foundation models and LLM building blocks.
- Understand transformers for vision and text, tokenisation, attention mechanisms, and prompting techniques.
- Understand Supervised Fine-Tuning (SFT), Reinforcement Learning from Human Feedback (RLHF), and Retrieval-Augmented Generation (RAG).
- Have hands-on experience with ML frameworks and libraries such as PyTorch, TensorFlow, and Hugging Face.
- Have experience building or working on AI applications in one or more domains: text, vision, speech, audio, or video.
- Be able to translate complex engineering requirements into structured specifications, implementation plans, and validation workflows.
- Bring strong problem-solving skills and an interest in using AI to improve engineering workflows and application development.
Responsibilities
- Translate ambiguous product and business ideas into clear, structured technical specifications.
- Create engineering specifications that serve as a source of truth for engineering teams and AI systems.
- Establish practices to reduce defects and operational issues caused by AI-generated code.
- Build, maintain, and scale production systems that leverage Generative AI.
- Design and monitor agentic workflows that connect AI components with traditional microservices.
- Validate automated outputs for security, performance, reliability, and intended functionality.
- Build observability into services and systems to identify and address production issues.
- Optimize inference costs through prompt engineering, model selection, tool usage, and workflow design.
- Collaborate with Product, Production Engineering, Security, and other stakeholders.
- Mentor junior engineers and help advance AI-assisted engineering and system orchestration.
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