Expert AI/ML Model Developer
24 hours ago
The position of AI/ML Engineer requires an individual with a strong foundation in computer science and expertise in designing, developing, and optimizing advanced AI/ML models for various applications.
The engineer will be responsible for designing and training models using naval-specific data and delivering them as end products. This involves fine-tuning open-source LLMs (e.g., LLaMA, Qwen, Mistral, Whisper, Wav2Vec, Conformer models) for Navy-specific tasks.
Key responsibilities include preprocessing, labeling, and augmenting datasets, implementing quantization, pruning, and compression for deployment-ready AI applications, and developing Large Language Models (LLMs) and translation models for mission-critical AI applications.
The engineer should possess hands-on experience with pretraining and fine-tuning methodologies such as Supervised Fine-Tuning (SFT), Instruction Tuning, Reinforcement Learning from Human Feedback (RLHF), and Parameter-Efficient Fine-Tuning (LoRA, QLoRA, Adapters).
Proficiency in building multilingual and domain-specific translation systems using techniques like backtranslation, domain adaptation, and knowledge distillation is essential.
The ideal candidate should demonstrate practical expertise with libraries such as Hugging Face Transformers, PEFT, Fairseq, and OpenNMT. Knowledge of model compression, quantization, and deployment on GPU-enabled servers is highly desirable.
Candidates must have a B Tech/M.Tech degree in Computer Science or related field with exceptional academic records. Professional ML certifications from Google, AWS, Microsoft, or NVIDIA are preferred.
Core skills and tools required include languages like Python and C++, frameworks like PyTorch and TensorFlow, and concepts like transfer learning, RAG, XAI, reinforcement learning, and optimized inference.
Experience requirements include hands-on experience with at least two major AI domains, deploying models in production environments, contributing to open-source AI projects, leading development of multiple end-to-end AI products, and scaling AI solutions for large user bases.
Product development skills required include end-to-end ML pipeline development, user feedback integration, cross-platform model deployment, and API design for ML model integration.
Cross-compatibility requirements include defining model interfaces, building CLI and REST-compatible inference tools, maintaining shared code libraries, and joint debugging and model-in-the-loop testing with UI and backend teams.
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