AI Engineer; LLM​/Agent Technologies

Albert’s mission is to digitalize the world of chemistry. Using data and machine learning, Albert enables R&D organizations to dramatically accelerate the invention of new materials. Our platform helps scientists and engineers build structured data foundations, digitize formulation and testing workflows, and apply AI to innovate faster, smarter, and at scale.

We are seeking an exceptional AI Engineer with a focus on LLMs and RAG systems. This role prioritizes designing and developing scalable, fault-tolerant AI systems while maintaining a strong focus on domain-specific AI solutions. You will play a critical role in building robust infrastructure to support high-performance applications and tools, enabling seamless data integration and transformation to power AI/ML capabilities in chemical and materials science.

You will partner closely with Product, Engineering, Scientific Architecture, and Design to translate complex scientific workflows into robust, explainable, and production-grade ML solutions. In addition to hands-on technical work, you will mentor other ML engineers and influence best practices across the organization.

Within the first 12–18 months, success in this role will look like:

Scalable AI System Development:

• Design, build, and maintain scalable, fault-tolerant AI systems leveraging OpenAI and Anthropic models.
• Develop RAG architectures to ensure efficient, high-performance information retrieval tailored to chemical and materials science.
• Optimize system performance to handle large-scale data and application demands.

AI Agent Development:

• Build and maintain intelligent AI agents using modern frameworks.
• Collaborate with domain experts to refine agent capabilities for specific scientific workflows.

Data Engineering and Integration:

• Architect and maintain vector database solutions for efficient data storage and retrieval.
• Develop pipelines for ingestion, transformation, and storage to enable AI/ML workflows.
• Collaborate with platform and ML engineers to integrate AI/ML models with backend systems.

System Reliability and Fault Tolerance:

• Implement robust error-handling, monitoring, and alerting mechanisms to ensure system resilience.
• Troubleshoot and resolve system bottlenecks and failures.

CI/CD and Deployment Pipelines:

• Design, implement, and maintain CI/CD pipelines for AI systems and data workflows.
• Promote automation and best practices to enhance the development lifecycle.

Adoption of Emerging Technologies:

• Stay informed on the latest trends and tools in AI/ML engineering and agent technologies.
• Introduce and implement new technologies to improve system scalability, data integration, and developer productivity.

Collaboration and Cross-Functional Teamwork:

• Work closely with AI/ML, data engineering, and platform teams to understand and deliver on technical requirements.
• Contribute to architectural decisions that impact the overall platform ecosystem.

• A strong passion for AI/ML engineering and scalable data systems.
• An ability to prioritize system scalability and fault tolerance while focusing on innovative AI/ML solutions.
• A collaborative mindset and excellent communication skills.
• A commitment to quality and innovation in AI and data engineering.
• A degree in Computer Science, AI, or a related field with 7+ years of industry experience (Bachelor’s) or 5+ years (Master’s or PhD) in software engineering, emphasizing expertise in building scalable, fault-tolerant AI systems.
• Advanced knowledge of modern AI frameworks (e.g., Lang Chain, Lang Graph, Auto Gen, Crew.ai).
• Experience with vector databases (e.g., Pinecone, Milvus, Pgvector, Chroma
  
  DB).
• Strong understanding of distributed systems and microservices architecture.
• Proficiency in REST API development using FastAPI, REST Framework or similar tools.
• Familiarity with cloud platforms (e.g., AWS, GCP, Azure) and containerization technologies (e.g., Docker, Kubernetes).
• Proven track record of deploying production-grade AI systems.
• Experience leading technical teams and fostering collaborative environments.

• Advanced degree in Computer Science, AI, or related fields.
• Background in chemical and materials…