Solutions Engineer (Data & AI)
Technical experts who design and demonstrate bespoke data and artificial intelligence solutions for enterprise clients.
Overview
A Solutions Engineer in the Data and AI sector focuses on translating abstract product features into concrete business value. The daily rhythm is defined by a mix of deep technical development and high-stakes communication, where the engineer might spend the morning coding a machine learning pipeline and the afternoon presenting it to executive stakeholders. They solve problems related to data scalability, model accuracy, and system integration, often working under the pressure of sales cycles and project deadlines.
Success in this role requires a blend of analytical rigor and situational awareness. Thriving individuals tend to be those who enjoy the variety of shifting between different industries and technical environments rather than focusing on a single internal product. The role feels like a constant cycle of learning and teaching, as the rapid evolution of AI technologies requires the engineer to continuously update their knowledge to maintain credibility with sophisticated clients.
Responsibilities
- Conduct technical discovery sessions to understand a client's existing data infrastructure and business challenges.
- Build and deliver customized demonstrations of AI platforms that address specific industry use cases.
- Develop functional proofs of concept using Python, SQL, and various cloud-based machine learning frameworks.
- Collaborate with product and engineering teams to provide feedback on market needs and technical limitations.
- Create architectural diagrams and technical documentation to support the transition from sale to implementation.
- Respond to technical requirements in Requests for Proposals and security questionnaires.
- Present technical strategies to both developer audiences and non-technical business leaders.
Qualifications
- A bachelor's or master's degree in Computer Science, Data Science, or a related quantitative field.
- Professional experience in software engineering, data engineering, or technical pre-sales.
- Proficiency in Python programming and working knowledge of machine learning libraries like PyTorch or TensorFlow.
- Extensive experience with SQL and cloud data platforms such as Snowflake, Databricks, or BigQuery.
- Demonstrated ability to explain complex technical concepts to non-technical stakeholders.
- Familiarity with cloud infrastructure providers such as AWS, Azure, or Google Cloud Platform.
Nice to have
- Advanced certifications in specialized areas such as Generative AI or MLOps.
- Prior experience working in a high-growth SaaS environment or management consulting.
- Knowledge of data governance, privacy regulations, and enterprise security standards.
- Experience with front-end technologies or BI tools to enhance demonstration interfaces.
Work environment
- Work is typically performed in a hybrid model, combining home office work with visits to client sites.
- Regular travel is often required to attend industry conferences and facilitate on-site workshops.
- The culture is collaborative, involving constant coordination with sales executives and product managers.
- Standard business hours are common, though deadlines during major deal closures may require additional time.
- Tools include cloud development environments, version control systems, and CRM platforms.
Benefits & growth
- Compensation often includes a base salary plus a performance-based commission or bonus structure linked to sales targets.
- Equity grants or stock options are common in both established technology firms and late-stage startups.
- Career progression typically leads to roles such as Principal Solutions Engineer, Solutions Architecture, or Sales Leadership.
- Professional development is often supported through sponsored certifications and attendance at global AI research conferences.
- The role provides a high degree of cross-functional exposure, facilitating transitions into Product Management or Technical Consulting.
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