Job Requirements
Remote
Secret Polygraph not specified
Mid Level Career (5+ yrs experience)
Salary not specified
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Job Description
Oteemo is seeking a Senior Data Engineer with deep AI/ML and Generative AI expertise to lead the design and delivery of production data and AI systems for our enterprise and public-sector clients. In this role you will own technical workstreams end to end — building robust data pipelines and agentic/GenAI capabilities, while mentoring engineers and driving engineering best practices across the team.
KEY RESPONSIBILITIES
● 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.
REQUIRED QUALIFICATIONS
● 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.
KEY RESPONSIBILITIES
● 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.
REQUIRED QUALIFICATIONS
● 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.
group id: 90929740
