Data Engineer

6 days ago

Dallas, TX, United States Harnham Full time
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.

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