Machine Learning Engineer

Position: Machine Learning Engineer Location: 100% Remote Pay Range: 65-75 an hour on W2 Independently (NOT AVAILBLE ON C2C/1099) Duration: 6 month contract to hire Job Description: We are seeking a Senior Machine Learning Engineer to design, build, and scale advanced AI systems using modern NLP and large language models. This role is ideal for someone who thrives at the intersection of deep technical research and real world production engineering. What You’ll Do • Design, train, fine tune, and deploy ML and NLP/LLM models for high volume, low latency applications • Build end to end ML pipelines, including data ingestion, OCR workflows, model training, evaluation, and deployment • Develop LLM fine tuning pipelines including full fine tuning, LoRA, prompt optimization, and DPO workflows • Architect Retrieval Augmented Generation systems using vector databases, embeddings, semantic search, and document indexing • Optimize models using quantization, compression, and distillation to improve speed and cost efficiency • Experiment with efficient LLM architectures and optimize for compute, memory, and accuracy • Leverage agentic coding tools (Cursor, Agentbricks, Agentspace) to accelerate ML development • Build robust MLOps frameworks including automated evaluation, monitoring, and feedback loops • Implement CI CD pipelines enabling rapid iteration from experimentation to production What You Bring • 5 plus years of experience in machine learning engineering with production deployments • Strong NLP experience including tuning and deploying LLMs • Excellent Python skills and experience building full stack ML systems • Deep knowledge of LLM adaptation methods such as LoRA, full fine tuning, prompt based techniques, and DPO • Experience improving inference performance and cost through optimization techniques • Understanding of RAG workflows including vector stores, embeddings, and retrieval systems • Experience with orchestration frameworks such as LangChain or LlamaIndex • Experience with cloud platforms such as GCP or Databricks • Familiarity with agentic coding tools supporting rapid ML development • Strong background in MLOps, automated evaluation, and CI CD for ML systems • Ability to work across research, experimentation, and production environments