Generative AI/ML Engineer

  • Washington D.C., DC
  • Posted 2 hours ago | Updated 2 hours ago

Overview

On Site
Depends on Experience
Contract - Independent
Contract - W2
Contract - 12 Month(s)

Skills

Artificial Intelligence
Collaboration
Cosmos-Db
Data Engineering
Data Science
Databricks
Extract
Transform
Load
Generative Artificial Intelligence (AI)
LangChain
Machine Learning (ML)
Microsoft
Microsoft Azure
NoSQL
NumPy
Orchestration
Pandas
Prompt Engineering
PySpark
Python
SQL
SQL Azure
Systems Architecture
Training
Vertex

Job Details

Hi,

We do have an urgent requirement for the below position with our direct client, Please submit Resume, Rate and Contact details.

Title: Developer Premium II - Generative AI/ML Engineer

Duration: Long Term Location: Washington, DC
5 Days onsite per week from Day1
About the Role:


The ideal candidate should have a strong system architecture background in the data science domain, solid Python programming skills, and hands-on experience with Azure OpenAI and cognitive service integrations. Experience with Agentic AI frameworks and Microsoft Copilot integrations is highly preferred.
Key Responsibilities:

  • Design, build, and optimize scalable data and ML pipelines in Python.
  • Develop, train, deploy, and monitor AI/ML and generative AI models using Azure Machine Learning and Azure OpenAI.
  • Collaborate with data scientists, solution architects, and product teams to deliver enterprise-grade ML solutions.
  • Hands-on experience with Azure OpenAI and Azure ML services (Data Factory, Databricks, Synapse).
  • Expertise in Azure technologies: Azure AD, Azure Search, Azure SQL, Cosmos DB, Azure OpenAI, and Cognitive Services.
  • Must have real-world experience in prompt engineering, fine-tuning GPT models, and building RAG-based solutions.
  • Proven experience with Agentic AI frameworks such as LangChain, LangGraph, or similar agent orchestration tools.
  • Solid experience in Python programming (Pandas, NumPy, PySpark, etc.).
  • Solid experience in data engineering (ETL, pipelines, SQL/NoSQL).
  • Practical knowledge of the ML lifecycle (training, deployment, monitoring).
  • Experience with Google Vertex AI or other enterprise AI platforms is a plus to have.

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