Lead AI Engineer – AI Foundations, LLM Core & Agentic AI/Remote

Job Title: Lead AI Engineer – AI Foundations, LLM Core & Agentic AILocation- RemoteRole SummaryWe are seeking a Lead AI Engineer to drive the design and development of advanced AI systems, with a focus on foundational models, Large Language Models (LLMs), and agentic AI frameworks. This role will lead the architecture and deployment of scalable, production-grade AI solutions that power intelligent automation and next-generation applications.Key ResponsibilitiesLead the design and development of AI/ML systems with a focus on LLMs and agent-based architectures.Build and optimize pipelines for training, fine-tuning, and deploying large language models.Architect AI platforms supporting retrieval-augmented generation (RAG), embeddings, and vector databases.Develop and manage agentic workflows, orchestration layers, and multi-agent systems.Integrate AI capabilities into enterprise applications via APIs and microservices.Ensure scalability, performance, and reliability of AI systems in production.Collaborate with data scientists, software engineers, and product teams to deliver AI-driven solutions.Establish best practices for prompt engineering, model evaluation, and responsible AI.Implement monitoring, logging, and continuous improvement for deployed AI systems.Stay up to date with advancements in generative AI, LLM frameworks, and tooling.Required Skills & Qualifications Bachelor’s or Master’s degree in Computer Science, AI, Machine Learning, or related field.7–10+ years of experience in software engineering/AI engineering, with strong hands-on expertise in ML systems.Deep understanding of:Large Language Models (LLMs) and transformer architecturesNLP, embeddings, and semantic searchRetrieval-Augmented Generation (RAG)Strong programming skills in Python (preferred) and experience with ML frameworks (PyTorch, TensorFlow).Experience with LLM ecosystems and tools (e.g., LangChain, LlamaIndex, vector DBs like Pinecone, FAISS).Knowledge of API design, microservices, and distributed systems.Experience with cloud platforms (Azure, AWS, or GCP) and MLOps practices.Preferred QualificationsExperience working with open-source and proprietary LLMs (e.g., GPT, LLaMA, etc.).Exposure to agentic AI frameworks and autonomous systems.Knowledge of fine-tuning techniques (LoRA, RLHF, etc.).Experience with Kubernetes, Docker, and scalable deployment architectures.Familiarity with AI safety, governance, and ethical AI principles.Key CompetenciesTechnical leadership and architecture designInnovation and problem-solving mindsetStrong collaboration and communication skillsAbility to translate business problems into AI solutionsMentorship and team developmentNice to HaveExperience building AI platforms or internal AI toolingContributions to open-source AI projectsBackground in data engineering or big data technologies