Software Engineer, AI Platform & Data Engineering

We are seeking a highly skilled Senior Software Engineer, AI Platform & Data Engineering to help scale and operationalize Generative AI, Predictive AI, and Data Science solutions across enterprise applications.

In this role, you will work closely with Data Scientists, Data Engineers, and Technology teams to transform experimental models and prototypes into secure, scalable, production-ready solutions. You will be responsible for designing, developing, deploying, and supporting AI-powered applications using modern cloud, container, and data engineering technologies.

The ideal candidate has deep expertise in Python development, AI/ML deployment, cloud-native architectures, and data engineering, with a passion for building reliable enterprise-grade AI solutions. Responsibilities

• Design, develop, and deploy scalable AI and machine learning solutions in production environments.
• Collaborate with Data Science teams to operationalize Generative AI, Predictive AI, and Machine Learning models.
• Build and maintain enterprise APIs, microservices, and application integrations.
• Develop and optimize data pipelines, ETL/ELT processes, and data workflows.
• Deploy solutions using cloud-native and containerized platforms including Azure and OpenShift.
• Implement software engineering best practices, including CI/CD, testing, monitoring, and automation.
• Build Retrieval-Augmented Generation (RAG) architectures, vector search solutions, and AI-powered applications.
• Ensure operational excellence through logging, monitoring, alerting, and production support.
• Partner with engineering and business stakeholders to deliver secure, scalable, and compliant solutions.
• Create technical documentation, runbooks, and operational support materials., Software Engineering & 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

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

Data Platforms

• Microsoft Fabric
• OneLake
• Data Warehouse Architecture
• Lakehouse Architecture

Orchestration & Automation

• Azure Data Factory
• Airflow or Similar Workflow Orchestration Platforms

Cloud, Containers & DevOps

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

Monitoring & Operations

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

Security & Governance

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

Analytics & Reporting

• Power BI

Nice to Have

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

What Success Looks Like

• Successfully deploys Data Science and Generative AI solutions into enterprise production environments.
• Builds scalable, maintainable, and secure AI-powered applications.
• Bridges the gap between Data Science experimentation and enterprise software engineering.
• Demonstrates strong expertise in Python, cloud-native architectures, container platforms, and data engineering.
• Drives engineering excellence through automation, observability, governance, and operational readiness.

• Bachelor’s degree in Computer Science, Engineering, related technical field, or equivalent practical experience.
• 7+ years of experience in Software Engineering, Data Engineering, Cloud Engineering, AI Engineering, or a related field.
• Experience developing production-grade applications using Python.
• Experience deploying Machine Learning, Predictive Analytics, or Generative AI solutions into production environments.
• Experience building REST APIs and distributed applications.
• Experience with cloud platforms and modern software deployment practices.
• Experience with container technologies such as OpenShift, Kubernetes, or Docker.
• Experience implementing CI/CD pipelines and DevOps practices.
• Experience working with SQL and large-scale data processing systems.

Preferred Qualifications

• Experience integrating Data Science solutions into enterprise applications.
• Experience operationalizing Generative AI applications using OpenAI, Azure OpenAI, or similar platforms.
• Experience supporting AI solutions within highly regulated environments.
• Experience supporting HR Technology, Workforce Analytics, or enterprise business functions.
• Strong understanding of AI governance, data privacy, security, and responsible AI practices., 1. Generative AI and Predictive AI production deployment experience 2. Strong Python engineering expertise 3. AI platform and application integration experience 4. OpenShift, containerization, and cloud engineering 5. Data engineering experience (SQL, ETL, Spark, Fabric) 6. Knowledge of AI/ML operationalization best practices

This role is ideal for a Senior Python Engineer, AI Engineer, AI Platform Engineer, Machine Learning Engineer, or Data & AI Engineer with a proven track record of turning Data Science and Generative AI prototypes into enterprise-scale production solutions. Writing Improvements

• Reorganized the content into a Google-style structure with clear sections: About the Role, Responsibilities, Qualifications, and Preferred Qualifications.
• Shifted focus from a staffing/job order format to a candidate-focused format.
• Consolidated repetitive technical requirements into logical skill categories.
• Emphasized impact, scalability, and business outcomes, which is characteristic of Google and other top-tier technology job descriptions.
• Removed contingent staffing language and intake-call notes to create a polished, market-ready posting.

Benefits Featured

Medical, Dental , and Vision Insurance is offered to qualified candidates.

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