Machine Learning Data Engineer (LLM & Generative AI)

Location: USA; Conditions: New York. Currently fully remote, with a future transition to a hybrid setup. 401(k) with employer matching is available for US-based employees.

Company

hirify.global is a Series C fintech company building an AI-powered conversational financial companion for Latinos in the US, with remittances, credit, savings, and wallet services.

What you will do

• Design, build, and maintain scalable automated pipelines for extracting, cleaning, and formatting datasets for LLM fine-tuning, including SFT, RLHF, and DPO.
• Develop telemetry and sampling systems for real-time production data and evaluate live LLM and agent performance, latency, and accuracy.
• Architect synthetic data generation workflows to augment training data, cover edge cases, and mitigate bias.
• Implement data quality checks, deduplication, and toxicity filtering for model-training data.
• Integrate data pipelines with model-training infrastructure and CI/CD workflows in partnership with ML Researchers and DevOps.
• Manage and optimize data flows into vector databases supporting RAG capabilities for agentic workflows.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related technical field.
• 5+ years of professional experience in Data Engineering, Machine Learning Engineering, or MLOps.
• Expert-level Python and advanced SQL proficiency.
• Hands-on experience with LLM APIs such as OpenAI and Anthropic, open-source models such as Hugging Face and Llama, and orchestration frameworks such as LangChain, LlamaIndex, or AutoGen.
• Experience using prompt engineering and programmatic techniques for synthetic data generation and validation.
• Strong experience with workflow orchestration tools.
• Experience with scalable GCP cloud infrastructure and distributed data processing frameworks.
• Equivalent competencies in the listed requirements will also be considered.

Nice to have

• Familiarity with vector search and vector databases such as Pinecone, Milvus, Weaviate, or Qdrant.
• Experience with LLM evaluation frameworks such as RAGAS, TruLens, or DeepEval, and automated LLM benchmarking suites.
• Understanding of privacy-preserving techniques, including PII redaction pipelines for sensitive ML training data.
• Experience deploying streaming data pipelines with Kafka, Flink, or Kinesis for real-time analytics, contextual data, agentic workflows, or streaming RAG systems.
• Bilingual English/Spanish proficiency.

Culture & Benefits

• Fully remote work currently, with a future transition to a hybrid setup.
• Initial stock options grant and annual performance bonus.
• Unlimited PTO and paid parental leave.
• Health, dental, and vision plans.
• 401(k) with employer matching for US-based employees.
• Continuous learning opportunities and growth in an entrepreneurial environment.