What are the responsibilities and job description for the ML Engineer / Data Engineer position at Jobs via Dice?
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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:
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.
- 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)
- 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.
Salary : $80 - $85
