Data Engineer

Engagement Details • Type: 12-month contract with potential for extension • Location: Fully remote; preference for contractors near a central U.S. hub who can occasionally work onsite (2-3 days/week) • Schedule: Approximately 40 hours per week (may begin at 32 hours) • Team Structure: Initial group of three engineers, with expected team growth • Reporting: Works closely with an engagement lead, account manager, and project manager • Compensation: Targeting $120-160/hr, with a maximum of $220/hr for top-tier profiles Core Responsibilities • Deliver end-to-end GenAI solutions, including requirements gathering, architecture, development, testing, deployment, and production support. • Build sophisticated RAG pipelines and LLM applications that integrate enterprise data and knowledge repositories. • Implement vector database solutions, agentic frameworks, and prompt orchestration systems to support AI-driven workflows. • Productionize GenAI applications using best practices for MLOps, CI/CD, automated pipelines, and performance monitoring. • Collaborate closely with data engineering and platform teams to design scalable, secure, and maintainable architectures. • Advise clients on tooling, design patterns, deployment strategies, governance, and operational readiness for GenAI systems. • Provide technical mentorship to internal and client teams adopting GenAI and modern data engineering practices. Key Technical Requirements • 5+ years of experience in AI/ML engineering, data engineering, software development, or similar technical fields. • 1-2 years of hands-on, production-level experience delivering GenAI solutions (RAG, LLMs, agentic workflows). • Strong experience with unified data and AI platforms (such as Spark, MLflow, feature stores, and data governance tools). • High proficiency in Python and common GenAI tooling: LangChain, LLM APIs (OpenAI, Azure, Anthropic), Hugging Face, and vector databases (FAISS, Pinecone, Weaviate, Chroma). • Solid experience with cloud infrastructure and deployment across AWS, Azure, or GCP, as well as DevOps practices (Git, pipelines, CI/CD). • Excellent communication skills and comfort operating in client-facing delivery environments. • Preferred: industry certifications in data engineering, ML engineering, or GenAI engineering.