Gen AI Engineer II

We are seeking a skilled Gen AI Engineer to develop, optimize, and deploy advanced LLMs, VLMs, and multimodal AI systems. You will work on fine-tuning foundation models, designing retrieval architectures, and building production-ready inference pipelines for scalable AI solutions. Key Roles

• Develop and enhance LLMs, VLMs, RAG systems, and multimodal generation pipelines for production use cases.
• Understand business requirements and convert them into scalable, high-performance AI model architectures and workflows.
• Fine-tune and customize Transformer-based models using proprietary datasets, advanced training strategies, and evaluation frameworks.
• Optimize tokenization, embedding generation, vector search, and retrieval flows for high-throughput applications.
• Develop high-performance inference pipelines using ONNX, TensorRT, quantization, batching, streaming, and GPU/accelerator optimizations.
• Ensure all models are production-grade robust, scalable, monitored, and integrated into backend systems.
• Research and evaluate cutting-edge architectures in multimodal models, generative AI, and retrieval-augmented techniques. Responsibilities
• Design end-to-end GenAI systems including training, fine-tuning, inference serving, and continuous model improvements.
• Work with backend teams to integrate models into scalable APIs using Triton, TensorRT, ONNX Runtime, vLLM, or custom inference engines.
• Build model evaluation pipelines BLEU, ROUGE, alignment tests, hallucination checks, safety filters, and latency/throughput benchmarks.
• Experiment with new architectures (Mixture-of-Experts, diffusion-based multimodal, etc.) and contribute to LLM/VLM improvements.
• Collaborate with product, backend, ML, and DevOps teams to deliver end-to-end GenAI features.
• Maintain documentation, ensure reproducibility, and follow best practices in model governance, versioning, and monitoring. Required Qualifications
• 4-6 years of experience in applied machine learning, deep learning, GenAI, or multimodal systems
• Proven expertise with Transformers, LLMs, VLMs, diffusion models, and retrieval-augmented systems.
• Hands-on experience with Python, PyTorch, TensorFlow, Hugging Face, LangChain, and modern training pipelines.
• Strong knowledge of vector databases (FAISS, Pinecone, Milvus, Chroma).
• Solid experience with ONNX, TensorRT, quantization, model optimization, and inference engines (vLLM, FasterTransformer, Triton).
• Understanding of distributed training, GPU utilization, mixed precision, and large-scale model serving.
• Strong problem-solving skills and ability to deliver production-quality AI systems.