About this role
The Role
As an AI Engineer, you will design and build production-grade AI solutions that solve complex customer and business problems. You will work across the full AI lifecycle from discovery and prototyping through evaluation, deployment, monitoring, and continuous improvement using the Databricks Platform. You will collaborate with customers, engineers, data scientists, product teams, and technical specialists to turn emerging AI capabilities into reliable, secure, and measurable business outcomes. The role is ideal for someone who enjoys hands-on engineering and customer collaboration while keeping pace with generative AI, LLMOps, and machine learning.
The Impact You Will Have
- Design and implement production-grade generative AI and machine learning applications.
- Build RAG, agentic, Text2SQL, fine-tuning, and multi-agent solutions.
- Develop data and AI pipelines using Python, SQL, Databricks, and cloud-native services.
- Establish evaluation strategies for quality, accuracy, safety, latency, and cost.
- Deploy, monitor, optimize, and troubleshoot AI workloads in production.
- Integrate models and applications with enterprise data, APIs, tools, and third-party systems.
- Apply best practices for governance, security, observability, reproducibility, and responsible AI.
- Serve as a trusted technical advisor to technical and business stakeholders.
- Collaborate with Product and Engineering teams to share feedback and influence product direction.
- Create reusable solution accelerators, reference implementations, notebooks, and documentation.
What We Look For
- 3+ years of experience in AI engineering, machine learning engineering, data science, software engineering, or a related technical role.
- Strong software engineering skills in Python and proficiency with SQL.
- Experience building and deploying AI/ML systems in production on AWS, Azure, or GCP.
- Practical experience with generative AI applications, including RAG, LLM workflows, agents, tool calling, prompt engineering, or fine-tuning.
- Familiarity with model evaluation, experimentation, monitoring, optimization, and MLOps.
- Experience with tools such as PyTorch, scikit-learn, pandas, Hugging Face, LangChain, or DSPy.
- Experience working with data platforms, vector search, APIs, and distributed data processing.
- Strong communication skills with both technical and non-technical audiences.
- Bachelor’s or master’s degree in computer science, engineering, statistics, mathematics, or a related discipline or equivalent practical experience.
Nice to Have
- Databricks certification or hands-on experience with Databricks AI capabilities.
- Experience with Unity Catalog, MLflow, Lakeflow, Mosaic AI, Model Serving, Vector Search, AI Gateway, Databricks Apps, or Lakebase.
- Experience with MCP, agent evaluation frameworks, MLflow Tracing, or online evaluation.
- Experience presenting technical work at conferences, workshops, meetups, or customer events.
- Background at a data or AI company, cloud provider, technical consulting firm, or research organization.