The Senior Engineer – AI/ML designs, builds, and deploys software solutions that integrate machine learning and artificial intelligence into production systems. This role bridges traditional software engineering and data science by developing scalable architectures, implementing ML pipelines, and optimizing model performance in real‑world applications.
The Senior Engineer collaborates closely with data scientists, product managers, enterprise architects, and platform engineers to translate business problems into reliable, efficient, and maintainable technical solutions. Strong skills in software engineering, cloud infrastructure, and applied machine learning are essential, along with a passion for experimentation, automation, and continuous improvement.
Primary Focus: Embedding ML models into production systems.
Key Focus Areas
- Design ML pipelines, feature engineering workflows, and model deployment strategies
- Partner with data scientists to productionize models and AI capabilities
- Ensure scalability, latency optimization, observability, and monitoring for ML‑driven services
Role Overview
This role is responsible for leading and contributing across the full software development lifecycle for products our customers love. In addition to hands‑on engineering, the Senior Engineer serves as a technical leader—pairing with other engineers and architects, supporting questions from other product teams, and encouraging cross‑team collaboration.
The role also involves building foundational, reusable software components and defining software‑level objectives to ensure product reliability, performance, and long‑term maintainability.
Duties and Responsibilities
Software & Architecture
- Lead and contribute to software design, development, and production support for AI/ML‑enabled products
- Build rapid prototypes to assess solution viability aligned to product strategy; scale and productionize approved solutions
- Create foundational, reusable code components for use across the organization
- Collaborate with Enterprise Architects and Chief Architecture Owners to produce architecture diagrams and documentation for security and compliance reviews
- Define and maintain service level objectives (SLOs) to measure system reliability and guide backlog prioritization
- Design and review API specifications and data contracts for shared services and cross‑team integrations
AI / ML Engineering
- Integrate AI, ML, and data science products into production systems
- Develop APIs to serve ML and LLM models, implement inference pipelines, and integrate external AI services
- Build data pipelines for training and inference (ETL, feature engineering, logging, and telemetry)
- Train and fine‑tune ML models or LLMs as needed; implement monitoring and evaluation strategies (e.g., drift detection, performance tracking)
- Support experimentation workflows including hyperparameter tuning, cross‑validation, and experiment tracking
Reliability, Observability & Operations
- Automate infrastructure, monitoring, and testing via custom code or scripts to ensure resiliency in production
- Create dashboards, logging, alerting, and response mechanisms to proactively identify and address issues
- Proactively monitor production and lower‑environment performance relative to defined SLOs
- Review system performance and capacity across code, infrastructure, data, and message processing layers
- Triage high‑priority incidents and outages as they arise and provide application support for production systems
Collaboration & Leadership
- Identify and share technical solutions that can be leveraged across teams and product lines
- Partner with product teams to identify customer‑facing and technical enhancements that improve user experience
- Mentor developers and provide technical guidance to elevate team capabilities
- Work with vendors and open‑source communities to identify and implement relevant platform and feature enhancements
Education, Experience & Qualifications
- 3 years of hands‑on software development experience
- 2 years of experience mentoring engineers or providing work direction
- Proficiency in one or more of the following: Java, Python, JavaScript, TypeScript, SQL
- Experience with SQL and NoSQL databases
- Familiarity with Agile development tools such as Atlassian Suite (Jira, Confluence, Bitbucket) or equivalents
- Strong understanding of cloud‑based architectures and production‑grade systems
Functional Skills
- Strong analytical skills with the ability to use data to drive operational excellence
- Effective planning and prioritization, focused on highest‑impact outcomes
- Solid financial and business acumen
- Clear, candid communicator across technical and non‑technical audiences
- Proven problem‑solving skills using structured, logical, and innovative approaches
- Continuous improvement mindset with an ability to adapt in fast‑paced environments
- Ability to influence and communicate effectively with senior leaders
- Demonstrated success working cross‑functionally across multiple initiatives
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