Lead Machine Learning Engineer

At NobleAI, we believe that energy, material science and chemistry are key to building a sustainable world and that artificial intelligence is essential to unlock this potential. NobleAI leverages innovative Science-Based AI technology to revolutionize energy workflows, materials development, and chemical designs. We enable companies to accelerate innovation and reduce costs in developing sustainable technologies and products.

We're a team of excellence-driven individuals who value thoughtfulness and respect while focusing on delivering products that empower engineers and researchers to create better solutions faster.

At NobleAI, we are developing the next generation of intelligent chemical informatics platform. Our goal is to create a seamless, intuitive experience that empowers users to achieve unprecedented productivity in data processing, visualization, and model building. We are seeking a forward-thinking team member who thrives on innovation, collaboration, and rapid iteration, and has the ability to solve challenging problems in science and technology.

As a Lead Machine Learning Engineer specializing in conversational and agentic systems at NobleAI, you will be responsible for architecting, building, and deploying intelligent features to our VIP platform. This role is ideal for individuals passionate about the cutting edge of LLMs and eager to build AI systems that can reason, plan, and act.

Join us in building a more sustainable world through the power of AI and scientific innovation.

Requirements

• Design domain specific AI systems and chatbots capable of complex dialogue management and workflow execution via tools, API calls and multi step tasks based on user goals, multi-agent orchestration.
• Collaborate with scientists to assess, fine-tune, and deploy LLMs on domain specific data to support accuracy measurement for use cases
• Build and maintain Retrieval-Augmented-Generation (RAG) systems, Reinforcement Learning frameworks, guardrail and assessment mechanisms for end to end lifecycle for customized models.
• Collaborate with product and software engineers to integrate the features into our platform.
• Establish prompt engineering and data management best practices for transparency and governance.
• Establish best practices for monitoring and evaluation of data and models across the model lifecycle (development, testing, and production)
• Keep a pulse on the latest advancements in NLP, LLMs, and agentic AI research, and act as a subject matter expert on architecture decisions on platform and use cases.

  What We’re Looking For

• MSc (preferred) or BSc in Computer Science, Artificial Intelligence, or a related quantitative field.
• 5+ years of hands-on experience building and deploying AI/ML systems, with a strong focus on Natural Language Processing (NLP).
• Proven experience designing and shipping chatbots, virtual assistants, or agentic systems using modern LLM-based architectures.
• Strong programming proficiency in Python (5+ years) and deep experience with core ML/NLP libraries such as PyTorch, TensorFlow
• Hands-on experience with LLM agent frameworks for building complex, tool-using applications, multi-agent orchestration, or establishing MCP services for platform capabilities.
• Demonstrated experience with Retrieval-Augmented Generation (RAG), including the use of vector databases like Pinecone, Weaviate, or ChromaDB.
• Familiarity with techniques for fine-tuning LLMs (e.g., LoRA/QLoRA) and experience working with open-source models (e.g., Llama, Mistral) or major model APIs (e.g., OpenAI, Anthropic).
• 5+ years of experience with cloud platforms (Azure preferred) and familiarity with deploying AI models as scalable microservices using Docker and Kubernetes (KFP, KServe).
• Solid software engineering fundamentals, including version control (Git), automated testing, and CI/CD principles.
• Excellent communication skills with the ability to articulate complex technical ideas to both technical and non-technical stakeholders.

Benefits

We offer great pay & benefits.

• Benefits coverage including medical, dental, vision, disability, and life insurance
• Retirement fund employer contribution
• Generous Paid Time Off & Holidays
• Stock options
• Performance-based bonus
• Salary range depending on experience