Senior Engineer, Data and AI

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🔥 1 minute ago

🦀 Maryland – Remote

🦀 Maryland – Remote

⏰ Full Time

🟠 Senior

🚰 Data Engineer

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Cisive

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 1977

💼 Consulting

⚖️ Legal

📦 Logistics

Consulting • Legal • Logistics

Cisive is a compliance-focused talent screening and workforce risk management company that provides background checks, drug and occupational health testing, ongoing monitoring, electronic I-9, and executive intelligence through a single, integrated platform. They serve highly regulated industries—particularly healthcare and transportation—offering specialized products like PreCheck for healthcare and Driver iQ for driver screening, and emphasize fast, accurate results and regulatory compliance for employers.

📋 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.

🎯 Requirements

• 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. • 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. • Proficiency in feature extraction/transformation and model selection, training, and evaluation. • Experience with LangChain and LangGraph for building agentic/AI workflows, and working with LLM APIs such as the Claude and OpenAI SDKs. • Experience self-hosting and serving models with vLLM is a plus. • 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 (experiment tracking) and LangSmith/LangFuse (LLM tracing and evaluation). • 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.

🏖️ Benefits

• 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.