AI Platform Engineer

We are seeking a Senior AI Platform Engineer to help scale and productionize Generative AI, Predictive AI, and Data Science solutions within an enterprise environment. In this role, you will partner with Data Scientists, Data Engineers, and Technology teams to transform AI and machine learning prototypes into secure, scalable, production-grade applications.

You will be responsible for designing, building, deploying, and supporting AI-powered solutions using modern cloud, container, and data engineering technologies. The ideal candidate combines strong Python engineering expertise with experience in AI/ML operations, cloud platforms, API development, and enterprise software delivery. Responsibilities

• Design, develop, and deploy scalable AI, Machine Learning, Generative AI, and Predictive AI solutions in production environments.
• Collaborate with Data Scientists to operationalize models and AI applications for enterprise use.
• Build and maintain Python-based services, APIs, and application integrations.
• Develop and optimize data pipelines, ETL/ELT processes, and data engineering workflows.
• Implement Retrieval-Augmented Generation (RAG), vector search, embedding, and LLM-based solutions.
• Deploy applications using cloud-native and containerized platforms including Azure, OpenShift, and Docker.
• Establish CI/CD pipelines and automation to support reliable software releases.
• Monitor, troubleshoot, and support production AI applications and services.
• Ensure compliance with enterprise security, governance, privacy, and data management standards.
• Create technical documentation, operational runbooks, and deployment guidelines.
• Partner with cross-functional teams to deliver solutions using Agile methodologies., Programming & Application Development
• Python
• Git
• FastAPI
• REST APIs
• JSON
• Pydantic
• C#/.NET integration

AI & Machine Learning

• Azure OpenAI
• OpenAI APIs
• LangChain / LangGraph
• Generative AI
• Predictive AI
• Machine Learning deployment
• Embeddings
• Vector Search
• Retrieval-Augmented Generation (RAG)

Data Engineering & Analytics

• SQL
• Oracle
• ETL / ELT
• Spark / PySpark
• Data Modeling
• Data Pipeline Development
• Power BI

Data Platforms

• Microsoft Fabric
• OneLake
• Data Warehouse
• Lakehouse Architecture

Cloud, DevOps & Platform Engineering

• Azure
• Docker
• OpenShift
• Kubernetes
• Azure DevOps
• GitHub Actions
• CI/CD

Security & Governance

• Key Vault
• OAuth
• Secrets Management
• Data Governance
• PII Protection

Monitoring & Operations

• Logging
• Monitoring
• Alerting
• Application Insights
• Production Support

Nice to Have

• C# / .NET Development
• Flask or Django
• React
• Gradio
• Databricks
• Neo4j / Graph Databases
• Semantic Kernel
• MLflow
• Snowflake
• Elasticsearch
• Azure AI Search

What Success Looks Like

• Successfully productionize Generative AI and Predictive AI solutions.
• Transform Data Science prototypes into scalable enterprise applications.
• Deliver secure, reliable, and maintainable AI platforms.
• Improve operational efficiency through cloud-native engineering practices and automation.
• Enable business teams to leverage AI solutions at enterprise scale.

Hiring Manager Priorities

1. Production deployment of Generative AI and Predictive AI solutions.
2. Strong Python engineering expertise.
3. Experience operationalizing Data Science and AI models.
4. OpenShift, containerization, and cloud platform experience.
5. Data engineering experience including SQL, ETL, Spark, and Fabric.
6. Strong understanding of AI and Data Science best practices.

• Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field, or equivalent practical experience.
• 7+ years of experience in Software Engineering, Data Engineering, Cloud Engineering, AI Engineering, or Machine Learning Engineering.
• Experience developing applications using Python.
• Experience deploying and operationalizing Machine Learning, Predictive AI, or Generative AI solutions.
• Experience designing and developing REST APIs and enterprise applications.
• Experience working with cloud platforms and modern software deployment practices.
• Experience with container technologies such as Docker, Kubernetes, or OpenShift.
• Experience implementing CI/CD pipelines and software engineering best practices.
• Experience with SQL, ETL/ELT processes, and data pipeline development.

Preferred Qualifications

• Experience integrating Data Science and AI solutions into enterprise applications.
• Experience supporting large-scale AI solutions in regulated or compliance-driven environments.
• Experience with Generative AI frameworks such as LangChain, LangGraph, Semantic Kernel, or similar technologies.
• Experience implementing Retrieval-Augmented Generation (RAG), vector search, embeddings, and LLM-powered applications.
• Experience with Azure OpenAI, OpenAI APIs, and cloud-native AI services.
• Experience with Microsoft Fabric, OneLake, Spark/PySpark, or modern analytics platforms.
• Experience supporting HR Technology, Workforce Analytics, or Data Science organizations.