Note: The job is a remote job and is open to candidates in USA. Oteemo is a technology consulting firm focused on cloud-native, enterprise DevSecOps, and AI-driven transformation. The Senior Data Engineer will design and implement end-to-end AI features, build secure scalable data pipelines, develop GenAI, agentic, ML, and BI systems, and implement MLOps/LLMOps practices across major cloud platforms. The role also owns technical workstreams, mentors junior engineers, and ensures reliable, secure, and governed solutions.
Responsibilities
- Help design and implement 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
Skills
- Active Secret or Top Secret clearance required
- 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
- Expert-level proficiency in Python and SQL, with the ability to work in polyglot environments (Scala, Java) as required by client enterprise 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.)
- 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
- Experience implementing robust data security and governance controls, including managing PII/PHI, authentication, and role-based access control (RBAC)
- 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)
- Exceptional time management and the ability to own technical workstreams autonomously
Benefits
- Full-time employment
- Remote work arrangement
- Continuous learning
- Collaborative and inclusive culture
Company Overview