Machine Learning Engineer designing and implementing AI systems focused on Japanese language challenges at Woven by Toyota. Involves technical R&D, system design, and collaboration with cross - functional teams.
About the role
- AI/ML Engineer at Sprout, responsible for designing, building, and deploying intelligent systems and AI agents. Collaborating with teams to enhance products with advanced AI solutions.
Responsibilities
- As an AI/ML Engineer, you will help design, build, and deploy intelligent systems and AI agents that power next-generation experiences across our products.
- Work closely with senior engineers and cross-functional teams to implement agentic workflows, orchestration pipelines, and model integrations using frameworks such as LangChain, LangGraph, and other emerging AI toolkits.
- Develop AI agents, API wrappers, and microservices that connect various systems, enabling contextual reasoning and automation.
- Assist in fine-tuning and deploying machine learning or foundation models where applicable and ensure that AI components are reliable, scalable, and aligned with responsible AI principles.
- Support efforts to maintain these pipelines, modernize APIs for AI consumption, and help implement Model Context Protocol (MCP) or similar interoperability layers to enhance agent-to-system communication.
Requirements
- 2–4 years of experience in AI engineering, software development, or intelligent systems, preferably in applied AI or workflow automation projects.
- Hands-on experience building or integrating AI agents, chatbots, or intelligent workflows, ideally using frameworks such as LangChain, LangGraph, LlamaIndex, or similar.
- Proficiency in Python (FastAPI) and experience working with modern software development practices (version control, testing, CI/CD).
- Practical understanding of API design and integration, including REST, gRPC, or GraphQL standards.
- Familiarity with machine learning concepts, model fine-tuning, embeddings, and evaluation methods.
- Basic experience with LLM operations, prompt engineering, or retrieval-augmented generation (RAG) setups.
- Exposure to cloud environments (Azure, AWS, GCP) and managed AI/ML services.
- Understanding of DevOps/MLOps fundamentals, including CI/CD, environment automation, and telemetry.
- Strong analytical and problem-solving skills, with the ability to collaborate effectively with cross-functional technical teams.
- Curiosity and willingness to stay updated with emerging agentic AI, orchestration, and interoperability frameworks such as MCP or Semantic Kernel.
Benefits
- Equal Opportunity Employment
- Welcomes applications from all sectors of society
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