AI Engineer

Job Description 

Applied AI Engineering & Prototyping:

• Rapidly develop and deploy MVPs and AI capabilities with the data science team.
• Design, build, and optimize AI models for real-world applications.
  Apply advanced techniques in meta-learning, multi-task learning, in-context learning, and prompt engineering.

Model Training, Optimization, & Evaluation:

• Train, fine-tune, and evaluate models using industry-standard techniques.
• Optimize AI systems for performance, efficiency, and scalability.
  Implement rigorous benchmarking and performance monitoring for deployed models.

Job Requirements

Skills & Expertise:

• Deep Learning & AI Frameworks: Proficiency in PyTorch, PyTorch Geometric, and Hugging Face.
• Graph Theory & Complex Networks: Experience with NetworkX, Graph Theory, and Complex Networks Theory.
• Neural Network Architectures: Strong knowledge of Transformers, LSTMs, RNNs, GNNs, Heterogeneous Graph Neural Networks (HGNNs).
  Reinforcement Learning & AI Reasoning: Expertise in reinforcement learning, agentic AI, and AI-driven decision-making.
• Model Engineering: Knowledge of meta-learning, multi-task learning, in-context learning, prompt engineering, model training, model optimization, and model evaluation.
  
  

Business & Strategic Thinking:

• Ability to bridge the gap between AI research and business needs.
  Strong problem-solving and critical-thinking skills.
  
  

Proficient in ML/AI and Data Engineering Tools:

• VLLM open source
• Nvidia NIMS
• Nvidia Triton
• PyTorch

Proficient in python coding and comfortable working with open-source libraries, LLM, computer vision etc. to support product development, turnkey projects, and AI POC.