• Research, design, and develop advanced AI/ML solutions across NLP, computer vision, reinforcement learning, and multimodal systems.
• Build, train, fine-tune, and evaluate LLMs, transformer-based architectures, and custom deep learning models.
• Develop end-to-end ML pipelines, including data ingestion, preprocessing, feature engineering, training, validation, and deployment.
• Prototype new AI capabilities; conduct experiments to validate feasibility, performance, and scalability.
• Optimize models for latency, throughput, GPU efficiency, and production-readiness.
• Collaborate with software engineering teams to integrate AI models into cloud-native platforms and microservices.
• Stay up to date with cutting-edge AI research (arXiv, NeurIPS, ICML, ACL, CVPR, etc.) and contribute insights to technical roadmaps.
• Evaluate and benchmark emerging open-source models, frameworks, and tooling.
• Maintain high-quality documentation, experiment tracking, and reproducible research workflows.

• Bachelor’s or Master’s in Computer Science, Machine Learning, Data Science, or related fields (PhD is a plus).
• Strong proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow.
• Hands-on experience with:
  • LLMs / Transformers (HuggingFace, OpenAI, Mistral, LLaMA, Qwen, etc.)
  • Training, fine-tuning, or distillation of medium-to-large-scale models
  • Prompt engineering and retrieval-augmented generation (RAG)
  • Reinforcement learning or sequential decision-making models
  • Computer vision or multimodal architectures (preferred, not mandatory)
• Solid understanding of ML fundamentals: supervised/unsupervised learning, optimization, evaluation metrics.
• Experience with vector databases, embeddings, and text-processing pipelines.
• Ability to design reproducible experiments and interpret research papers.
• Familiarity with Docker, Linux, and cloud platforms (Azure, AWS, or GCP).
• Experience deploying models through APIs, microservices, or GPU inference servers.
• Strong English skills with the ability to comprehend technical documents, write clear reports, and communicate effectively in a professional setting.

Nice-to-Have

• Experience with distributed training frameworks (DeepSpeed, PyTorch Lightning, Ray, Colossal-AI).
• Knowledge of CUDA, GPU profiling, or model optimization/quantization (GGUF, TensorRT, ONNX).
• Experience building agentic workflows, autonomous AI agents, or tools-based LLM systems.
• Background in DevOps/MLOps: CI/CD for ML, model monitoring, A/B testing.
• Contributions to open-source AI projects or research publications.