AI Engineer

• This is the first software engineering hire at Otonomee and a key role in building the company’s AI capability and platform
• You will report to the CTO, with a dotted line to the Product Builder
• You will design, build, and ship AI-powered applications end-to-end — spanning backend, data, APIs, and lightweight UI — while establishing the foundation of the Enterprise AI Platform on Azure
• In the early phase, this role is highly hands-on
• You will turn ambiguous business problems across multiple teams into working solutions using AI-native development tools
• As the team grows, the role will evolve toward platform architecture, integrations, and technical leadership

Full Stack AI Engineer

Machine Learning Engineer

• Develop and maintain a solid infrastructure within the Data Science team focusing on DevOps processes
• Establish and extend tools to automate the Data Science Tech Stack to enhance MLOps processes
• Ensure reliable and scalable deployment foundation for ML models
• Collaborate effectively with stakeholders to achieve business goals
• Build and maintain infrastructure as code using Terraform

Senior Machine Learning Engineer

• Define and lead the architecture for scalable Machine Learning and AI platforms.
• Design end-to-end ML workflows using Databricks, including: Feature engineering, Model training, Experimentation, Deployment, Monitoring
• Architect scalable data pipelines for AI/ML workloads using:, Apache Spark, Python, SQL
• Establish MLOps best practices including:, CI/CD for ML, Model versioning, Model governance, Automated retraining, Model drifting, Observability and monitoring
• Design secure and compliant AI architectures aligned with governance and privacy standards.
• Partner with Data Engineering teams to optimize data models and feature stores.
• Guide Data Scientists and ML Engineers on scalable production design patterns.
• Evaluate and integrate modern AI capabilities, including (this will be a plus): LLMs, Vector databases, Retrieval augmented generation (RAG), AI agents
• Drive cost optimization, scalability, and operational excellence across ML platforms.
• Define reference architectures and best practices across multiple ML teams (not just owning a single project).
• Support stakeholder engagement and translate business needs into scalable technical solutions.

Machine Learning Architect

• System Design & Development: Architect and develop an agentic AI platform. Use this platform to implement the initial agentic workflows capable of automating complex processes in areas including Finance, HR, Legal, Customer Support, and Operations. Mentor more junior agentic AI developers to further expand such flows.
• Tool Integration: Work with a variety of AI tools and platforms (e.g., Claude, AWS QuickSight, OpenAI, n8n, LangChain, etc.) to build seamless, integrated solutions.
• End-to-End Automation: Develop and deploy end-to-end solutions that include data ingestion, processing, decision-making, and action execution.
• Custom AI Models: Fine-tune and customize AI models to meet specific business requirements, ensuring high accuracy and reliability.
• Collaboration: Collaborate with cross-functional teams, including data scientists, engineers, and business stakeholders, to understand requirements and deliver impactful solutions.
• Performance Optimization: Continuously monitor, evaluate, and optimize the performance of agentic systems to ensure scalability, efficiency, and robustness.
• Documentation & Reporting: Maintain clear documentation of system architecture, workflows, and processes. Provide regular updates and reports to stakeholders on system performance and outcomes.
• Innovation: Stay updated on the latest advancements in AI, machine learning, and automation technologies, and proactively identify opportunities to enhance existing systems.

Lead AI Engineer

• Own the AI layer of the product
• Expand system capabilities by adding new use cases and data sources
• Improve multi-step workflows and system interaction
• Enable external systems to query the platform via APIs
• Refactor existing systems as needed

Senior AI Engineer – LLM, Multi-Agent Systems

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Machine Learning Engineer, Recommendations

• Be the go-to authority on how our data community adopts and maximizes Databricks
• Set standards, build guidance, and cultivate a thriving community of data scientists
• Champion Databricks capabilities across all data science teams
• Organize and deliver internal community trainings and hands-on workshops
• Build and nurture a community of practice around AI/ML
• Present at internal tech forums and represent the platform vision to senior leadership
• Define group-wide guidelines for applying generative AI vs traditional ML approaches
• Establish MLOps and AI Ops standards covering model lifecycle, monitoring, governance, and deployment patterns
• Produce high-quality technical blueprints, reference architectures, and step-by-step implementation guides
• Act as a bridge between platform team and data science community

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