Machine Learning Data Engineer (LLM & Generative AI)

Felix Technologies•New York, NY

1d•Remote

About The Position

Félix is seeking an innovative, highly technical Machine Learning Data Engineer to build the data infrastructure powering its large language models (LLMs) and autonomous AI agents. The role covers the end-to-end data lifecycle, including real-time telemetry and pipelines that train and refine models. The engineer will ensure models receive high-quality, diverse, well-structured data to drive ongoing performance improvements. The position works closely with ML Researchers and DevOps to integrate data pipelines with model-training infrastructure and support continuous integration and deployment of model updates. The job is currently fully remote, with a planned future transition to a hybrid setup.

Requirements

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

Nice To Haves

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 building automated LLM benchmarking suites.
Understanding of privacy-preserving techniques, including PII redaction pipelines, for sensitive data used in ML training.
Experience deploying streaming data pipelines with Kafka, Flink, or Kinesis for real-time analytics and for feeding real-time contextual data into agentic workflows and streaming RAG systems.
Bilingual English/Spanish.

Responsibilities

Design, build, and maintain scalable automated pipelines to extract, clean, and format datasets for LLM fine-tuning, including Supervised Fine-Tuning, RLHF, and DPO.
Develop telemetry and sampling systems to ingest production data and evaluate and benchmark live LLMs and AI agents for performance, latency, and accuracy.
Architect and scale synthetic data generation workflows using foundational models to augment training data, cover edge cases, and mitigate bias when real-world data is scarce or privacy-restricted.
Implement automated data-quality checks, deduplication strategies, and toxicity filtering for model-training data.
Partner with ML Researchers and DevOps to integrate data pipelines with model-training infrastructure and enable CI/CD for model updates.
Manage and optimize data flows into vector databases to support Retrieval-Augmented Generation (RAG) for agentic workflows.