Agentic AI- AI/ML Engineer
Location: Remote
Contract
H1B will work.
Primary Responsibilities:
- AI/ML Development and Deployment:
- Architect and implement scalable ML solutions using Azure ML Studio and Azure Machine Learning services
- Fine-tune and deploy LLMs for enterprise-grade applications
- Design and build RAG pipelines leveraging Azure services and vector databases
- Develop and orchestrate multi-agent LLM systems for complex workflows
- Ensure robust model deployment, monitoring, and performance optimization in production environments
- Azure Platform Engineering:
- Leverage Azure services such as Azure Data Lake, Azure Synapse, Azure Blob Storage, and Azure Cognitive Search for data ingestion and processing
- Integrate AI models with Azure-based data pipelines and APIs
- Use Azure DevOps for CI/CD of ML workflows and model versioning
- Apply MLOps best practices for model governance, monitoring, and retraining
- Ensure compliance with data governance, privacy, and security standards in cloud environments
- Data Engineering and Processing:
- Build and maintain ETL/ELT pipelines for structured and unstructured data using Databricks and Apache Spark
- Prepare and transform structured and unstructured data using Python, PySpark, and SQL for training and inference
- Collaboration and Innovation:
- Collaborate with data scientists, product managers, and engineers to deliver AI-driven features
- Stay current with advancements in generative AI, LLMs, and Azure AI services
- Contribute to the continuous improvement of AI/ML pipelines and best practices
Required Qualifications:
- Experience:
- 5+ years of hands-on experience in AI/ML engineering with a focus on Azure
- Proven experience in deploying ML models using Azure ML Studio and Azure Machine Learning
- Experience working with LLMs, RAG systems, and AI agents
- Technical Skills:
- Experience with Databricks, Apache Spark, and Azure Data services
- Familiarity with MCP, Langchain, vector databases and embedding models
- Knowledge of Azure DevOps and CI/CD for ML workflows
- Proficiency in Python, SQL, LLMs and PySpark
- Soft Skills:
- Proven solid analytical and problem-solving skills
- Proven effective communication and collaboration with cross-functional teams
- Demonstrated ability to translate business requirements into technical solutions
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