ML Engineer / Data Engineer

Overview

On Site
Depends on Experience
Accepts corp to corp applications
Contract - W2
Contract - 1 Month(s)

Skills

Analytics
Artificial Intelligence
Cloud Computing
Collaboration
Conflict Resolution
Continuous Delivery
Continuous Integration
Data Governance

Job Details

We are looking for ML Engineer / Data Engineer for our client in Princeton, NJ
Job Title: ML Engineer / Data Engineer
Job Location: Princeton, NJ
Job Type: Contract
Job Description:
Pay Range: $80hr - $85hr
Responsibilities:
  • Design and develop data ingestion pipelines from source systems using Azure Databricks and Azure Data Factory into the Azure Analytics Platform.
  • Build and optimize ETL/ELT pipelines for structured and unstructured data.
  • Develop and deploy LLM-powered applications, including RAG-based solutions for enterprise use cases.
  • Implement vector databases and embedding-based retrieval systems to support RAG workflows.
  • Integrate LLMs with Azure services (e.g., Azure OpenAI, Cognitive Search) for intelligent data processing and insights.
  • Apply Python, PySpark, and modern ML frameworks to build scalable AI solutions.
  • Provide technical design and coding guidance to the team to achieve project deliverables.
  • Ensure CI/CD integration using Azure DevOps for ML and data pipelines.
  • Collaborate with business stakeholders to gather requirements and translate them into technical solutions.
  • Stay current with AI/ML trends, including Generative AI, LLM fine-tuning, and prompt engineering.
Skills And Expertise:
  • Azure Data Factory, Azure Databricks, Azure DevOps, Azure Storage/Data Lake
  • ETL & ELT, Data Warehousing, SQL, Relational Databases
  • Python, PySpark, and ML frameworks (e.g., Hugging Face, LangChain)
  • LLM development and deployment (Azure OpenAI, or similar)
  • RAG architecture design, Vector Databases (e.g., Pinecone, Weaviate, FAISS)
  • Prompt Engineering, LLM Fine-tuning, and Model Evaluation
  • Experience with Cloud Platforms (Azure is a must)
Preferred Qualifications:
  • Experience with MLOps for LLMs, including model lifecycle management and monitoring.
  • Knowledge of semantic search, embedding optimization, and knowledge graph integration.
  • Familiarity with distributed systems and scalable AI architectures.
  • Understanding of data governance, security, and compliance in AI/ML solutions.
  • Strong problem-solving skills and ability to architect end-to-end AI solutions.
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