Data Engineer

Oteemo, Inc

United States

28 days ago

Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Expert

Experience required

5 years minimum

Working hours

Regular working hours

Job source

www.clearancejobs.com

Tech stack

Java (Programming Language) Artificial Intelligence Airflow Amazon Web Services Microsoft Azure Continuous Integration Information Engineering Extract Transform Load (ETL) Data Security Python (Programming Language) Machine Learning Role-Based Access Control

+16 more

Software Deployment SQL Databases Web Application Frameworks Google Cloud Large Language Models Prompt Engineering Apache Spark Generative AI Information Technology Low Latency Dask Machine Learning Operations Virtual Agents Terraform Data Pipelines Databricks

Job description

Lead the design and implementation of AI features end to end - from data pipelines through GenAI/agentic workflows to production deployment - applying sound judgment on model behavior, evaluation, reliability, and guardrails.
Build and optimize scalable, secure data pipelines and ETL/ELT workflows in Python and SQL across polyglot client enterprise environments.
Develop Generative AI, agentic, ML, and BI systems using RAG, embeddings, vector databases, and modern frameworks (Spark, LangChain, Databricks, Airflow, and similar).
Implement MLOps/LLMOps practices - CI/CD for data workflows, automated agent evaluation, and infrastructure as code across AWS, Azure, and GCP.
Enforce data security and governance, including PII/PHI handling, authentication, and role-based access control.
Own technical workstreams autonomously, mentor junior engineers, and champion software engineering best practices across the team.

Requirements

Education: Degree in Computer Science / Engineering, or equivalent experience.
Experience: 5+ years of relevant professional experience in a data engineering role, including leading technical workstreams, mentoring junior engineers, and driving adoption of software engineering best practices within a team.
Core languages: Expert-level proficiency in Python and SQL, with the ability to work in polyglot environments (Scala, Java) as required by client enterprise systems.
AI/ML systems: Strong experience building Agentic AI, Generative AI, Machine Learning, and Business Intelligence systems - including prompt design, retrieval-augmented generation (RAG), embeddings, vector databases, context construction, and output handling in production workflows using modern frameworks (Spark, LangChain, Databricks, Dask, Airflow, Dagster, Kedro, etc.).
End-to-end delivery: Ability to lead the implementation of AI features end to end, with sound judgment around model behavior, evaluation, reliability, guardrails, and the trade-offs between quality, latency, and cost.
Security & governance: Experience implementing robust data security and governance controls, including managing PII/PHI, authentication, and role-based access control (RBAC).
MLOps / LLMOps: Deep knowledge of MLOps/LLMOps, including CI/CD for data workflows, automated agent evaluation (LangSmith, Opik, Langfuse), and infrastructure as code (Terraform) across cloud providers (AWS, Azure, GCP).
Ownership: Exceptional time management and the ability to own technical workstreams autonomously.