[Hiring] Senior Engineer, Data and AI @Cisive

Role Description

This position has a wide range of responsibilities that includes both data and AI engineering. They will be responsible for designing and implementing data infrastructure to extract, clean, move, and store data. They will need the ability to independently develop AI/ML systems and products for both internal and external use. They will communicate with business stakeholders to understand their needs and develop solutions to address them.

Qualifications

• Degree in Computer Science, Physics, Mathematics, or a similar field; Master's degree a plus.
• 3–5 years of experience as a data engineer, ML engineer, AI engineer, AI infrastructure engineer, or in a similar role.

Requirements

• Strong communication skills to learn and collaborate with stakeholders across the organization.
• Strong, independent analytical and problem-solving abilities, and the internal drive to execute projects to completion.
• Ability to manage projects, prioritize tasks, and meet deadlines.
• Willingness to learn and adapt to changing business needs, requirements, and emerging technologies.
• Strong Python skills for building, training, and deploying both traditional ML models and modern AI applications — including LLM-based systems, RAG pipelines, and agentic workflows.
• Experience with LangChain and LangGraph for building agentic/AI workflows, and working with LLM APIs such as the Claude and OpenAI SDKs.
• Proficiency building and serving APIs with FastAPI, using Pydantic for data validation and schema enforcement.
• Solid grounding in statistical methods and experimental design (e.g., hypothesis testing, regression, causal inference) to validate models and ensure sound decision-making.
• Experience deploying, monitoring, and maintaining models and AI systems in production, using tools such as MLflow and LangSmith/LangFuse.
• Discover and characterize source data systems, understand and model the underlying business concepts, and build data models that organize data to meet operational and reporting needs.
• Proficiency with databases (T-SQL, NoSQL) — writing and optimizing tables, queries, and indexes for scalability, reliability, and performance.
• Design and implement pipelines to move and transform data between systems.
• Experience with data warehousing concepts and platforms like Databricks; familiarity with Spark and Python for large-scale data processing.
• Knowledge of cloud services, particularly Azure, for scalable data storage and processing.
• Awareness of data quality, privacy, security, and compliance best practices.

Company Description

It's fun to work in a company where people truly BELIEVE in what they're doing! We're committed to bringing passion and customer focus to the business.