My client is evolving and seeking talented individuals with a deep understanding of Large Language Models (LLMs), Vision-Language Models (VLMs), and agentic model architectures. The ideal candidate will possess a strong foundation in both research and engineering, with hands-on experience in developing, fine-tuning, and deploying AI systems.

The Role

You will be instrumental in building scalable, production-ready AI applications that integrate multimodal reasoning and advance the capabilities of autonomous intelligent agents.

Responsibilities

• Design, train, fine-tune, and optimize LLMs and VLMs for real-world applications.
• Develop and orchestrate autonomous agent frameworks capable of multi-step reasoning, planning, and tool use.
• Build scalable, low-latency inference systems for large models using frameworks like DeepSpeed, vLLM, TensorRT, or ONNX Runtime.
• Implement distributed training, model parallelism, and efficient inference pipelines; optimize deployment for edge devices, GPUs, and cloud-based platforms.
• Stay updated with the latest advancements in LLMs, multimodal models, and autonomous agents.

Requirements

• Strong understanding of LLMs, VLMs, transformers, and multimodal architectures.
• Experience with fine-tuning, LoRA/QLoRA, quantization, distillation, and evaluation.
• Knowledge of neurosymbolic methodology and reinforcement learning techniques (RLHF, RLAIF).
• Familiarity with frameworks such as LangChain, LlamaIndex, AutoGPT, CrewAI, OpenAI Agents, and Hugging Face Transformers/Agents.
• Proficiency in designing reasoning loops, memory systems, and multi-agent coordination.
• Strong skills in PyTorch, TensorFlow, Hugging Face, OpenAI APIs, DeepSpeed, and vLLM.
• Experience with vector databases like Weaviate, Pinecone, FAISS, Milvus, and tools like Redis and Kafka.
• Knowledge of Weights & Biases, MLflow, TensorBoard, and Evals frameworks.
• Proficient in Python for AI research, prototyping, and deployment pipelines.
• Experience with C++ for performance-critical components and model inference optimization.
• Proficient with Docker and AI distributed training systems.
• Strong knowledge of CUDA, GPU optimization, and high-performance computing.
• Familiarity with cloud platforms (AWS, GCP, Azure) and edge deployment strategies.

Qualifications

• Master’s or PhD in Computer Science, AI/ML, Robotics, or a related field.
• Proven track record of hands-on work with LLMs, VLMs, or agentic frameworks.
• Experience in productionizing AI systems at scale.
• Excellent communication and collaboration skills.

If you're ready to take your career to the next level, we encourage you to apply and be part of our innovative journey.