Artificial Intelligence/Machine Learning Engineer - LLM/Prompt Engineering

<p><p><b>Key Responsibilities : </b><br/><br/></p><p>- Design, implement, and optimize prompt engineering strategies to elicit high-quality responses from LLMs.<br/><br/></p><p>- Fine-tune Google LLMs (PaLM, Gemini) using domain-specific datasets for custom use cases.<br/><br/></p><p>- Apply Reinforcement Learning from Human Feedback (RLHF) to improve the performance and decision-making of AI agents.<br/><br/></p><p>- Develop and orchestrate intelligent agents using Vertex AI Agent Builder, AutoML, and MCP (Multi-Agent Control Protocol).<br/><br/></p><p>- Implement Agent-to-Agent (A2A) architectures and protocols to enable seamless multi-agent collaboration.<br/><br/></p><p>- Contribute to the design of scalable and robust agent architectures and evaluation frameworks.<br/><br/></p><p>- Collaborate with cross-functional teams including data scientists, product managers, and ML Skills & Qualifications : </b></p><p><br/></p>- Strong hands-on experience with Python, and ML frameworks like TensorFlow, JAX, and PyTorch.<br/><br/></p><p>- Deep understanding of LLM internals, including transformers, LoRA (Low-Rank Adaptation), quantization, and PEFT (Parameter-Efficient Fine-Tuning).<br/><br/></p><p>- Experience with Google Cloud AI ecosystem, including Vertex AI, Gemini, and AutoML.<br/><br/></p><p>- Practical knowledge of ADK, A2A orchestration, and MCP protocols.<br/><br/></p><p>- Experience in designing and optimizing chain-of-thought prompts, zero-shot, few-shot, and instruction tuning strategies.<br/><br/></p><p>- Strong problem-solving and algorithmic thinking to Have : </b></p><p><br/></p>- Experience with conversational AI, knowledge retrieval systems, or multi-turn dialogue agents.<br/><br/></p><p>- Familiarity with model compression, distillation, and accelerator optimization.<br/><br/></p><p>- Publications or hands-on projects in natural language processing, LLM customization, or RLHF pipelines.</p><br/></p> (ref:hirist.tech)