Machine Learning Engineer (AI Agents)

We are seeking an experienced Machine Learning Engineer specializing in Large Language Models (LLMs), Natural Language Processing (NLP), and AI Agents. In this role, you will design, develop, and deploy cutting-edge language models and agent-based systems that drive innovation and deliver value across products and services.

Key Responsibilities

• Design, fine-tune, and optimize large language models (LLMs) for specific use cases and domains
• Develop and implement prompt engineering techniques to enhance model capabilities and performance
• Create autonomous AI agents capable of performing complex tasks through language understanding and reasoning
• Build retrieval-augmented generation (RAG) systems that combine LLMs with external knowledge sources
• Implement evaluation frameworks to benchmark LLM and agent performance across various metrics
• Optimize models for production deployment with attention to latency, throughput, and cost
• Design conversational flows and dialogue management systems for AI assistants
• Develop techniques to ensure responsible AI deployment, including safety guardrails and bias mitigation

• Stay current with the rapidly evolving NLP/LLM research landscape and implement state-of-the-art techniques

   

Requirements

• Strong experience with transformer-based models and frameworks (Hugging Face, PyTorch, TensorFlow)
• Practical experience fine-tuning, serving, and optimizing LLMs (e.g., GPT models, Llama, Claude, Mistral)
• Expertise in NLP techniques including prompt engineering, embeddings, and semantic search
• Experience with vector databases and retrieval systems (Pinecone, Weaviate, Faiss, etc.)
• Familiarity with model quantization, distillation, and other optimization techniques
• Experience with LLM orchestration frameworks (LangChain, LlamaIndex, etc.)
• Working knowledge of cloud platforms (AWS, GCP, Azure) for ML model deployment
• Proficiency with containerization technologies (Docker, Kubernetes)

• Strong understanding of LLM evaluation methodologies and metrics

   

Nice-to-Have skills:

• Experience developing multi-agent systems or agent-based architectures
• Knowledge of reinforcement learning from human feedback (RLHF) techniques
• Familiarity with LLM inference optimization (vLLM, TensorRT, ONNX)
• Experience with tools for LLM observability, evaluation, and debugging
• Experience with multimodal models combining text, vision, and audio
• Knowledge of LLM security, safety, and alignment techniques