Senior Data & AI Engineer - ML, MLOps & GenAI

Cisive Inc.; United States; Presencial; USD 120 000 - 180 000; Tempo integral

Resumo da oferta

Cisive Inc. is seeking a data and AI engineer to design and implement data infrastructure for extracting, cleaning, moving, and storing data, while developing AI/ML systems for internal and external use.

You will translate business needs into scalable data and AI solutions, collaborating with stakeholders, and applying MLOps practices to deploy, monitor and improve models in production. Strong Python, cloud (Azure) and modern tooling experience are expected, with a focus on robust data

Qualificações

• B.S./M.S. in CS, Physics, Math, or related field; advanced degree a plus.
• 3–5 years in data/ML/AI engineering roles, with hands-on model deployment.
• Strong Python, ML, data engineering and cloud experience.

Responsabilidades

• Design and implement data infrastructure to extract, clean, move and store data.
• Independently develop AI/ML systems and products for internal and external use.
• Collaborate with stakeholders to translate needs into scalable data/AI solutions.

Conhecimentos

Communication

Independent Problem-Solving

Project Management

Adaptability

Machine Learning & AI Systems

GenAI Tooling

API Development

Statistical & Mathematical Rigor

MLOps/LLMOps

Data Modeling

Database Development & Optimization

ETL/ELT Pipelines

Big Data / Data Warehousing

Cloud Platforms

Data Governance & Security

Formação académica

Degree in Computer Science, Physics, Mathematics, or a similar field

Master's degree a plus

Ferramentas

LangChain

LangGraph

Claude SDK

OpenAI SDK

vLLM

FastAPI

Pydantic

MLflow

LangSmith

LangFuse

Databricks

Spark

Azure

T-SQL

NoSQL

Descrição da oferta de emprego

Cisive Inc. is seeking a data and AI engineer to design and implement data infrastructure for extracting, cleaning, moving, and storing data, while developing AI/ML systems for internal and external use.

You will translate business needs into scalable data and AI solutions, collaborating with stakeholders, and applying MLOps practices to deploy, monitor and improve models in production. Strong Python, cloud (Azure) and modern tooling experience are expected, with a focus on robust data