Enterprise AI (Multi-Disciplinary)

About the Role

We are seeking versatile, forward-thinking AI Platform Engineers to join our

enterprise AI engineering initiative. As we scale our central AI ecosystem, we

are hiring technical leaders and practitioners across three key specializations:

1. Cloud, Network & Infrastructure Operations
2. MLOps, Data Science & Generative AI Platforming
3. AI Architecture, Security & Governance

Core Focus Areas & Responsibilities

Depending on your specialization and expertise, you will focus on one of the

following key tracks:

MLOps & Data Science Infrastructure

• Architect, build, and deploy end-to-end AI/ML pipelines, automated model training, feature stores, and real-time inference decision engines.
• Build scalable infrastructure to support LLM fine-tuning, prompt engineering, RAG pipelines, red-teaming, and model evaluations.
• Establish automated CI/CD for ML workflows, model governance, performance monitoring, and drift detection across multi-domain datasets.

Platform Architecture, Security & Governance

• Design secure-by-design, enterprise-grade AI solution architectures, spanning data ingestion, model serving, and API orchestration across hybrid cloud environments.
• Implement AI security controls, Responsible AI (RAI), and Explainable AI (XAI) frameworks addressing LLM prompt injection, data leakage, adversarial risks, and OWASP standards.
• Integrate privacy-preserving mechanisms (encryption, anonymization, RBAC, tokenization) and perform threat modeling for AI ecosystems.

Platform Infrastructure, Operations & Networking

• Manage the availability, performance, observability, and capacity planning for hybrid cloud platform environments (Azure, Red Hat OpenShift, Kubernetes).
• Design and configure secure hybrid connectivity, cloud routing (BGP, VPN, ExpressRoute/DirectConnect), network segmentation, and API gateway architectures.
• Enforce Zero Trust principles, DevSecOps practices, disaster recovery plans, and incident response procedures for mission-critical AI workloads.

What We Are Looking For

Education & Experience

• Bachelor’s or Master’s degree in Computer Science, Computer Engineering, Data Science, Cybersecurity, Information Security, or a related quantitative field.
• 3-5 years of professional experience in MLOps, AI/ML engineering, cloud platform engineering, hybrid networking, or AI security architecture.

Preferred Skill Sets

• MLOps & Generative AI: Expertise in Python, SQL, Spark, Databricks, MLflow, Airflow, vector databases, LangChain/LangGraph, or Model Context Protocol (MCP).
• Cloud & DevOps: Hands-on experience with Azure, AWS, GCP, Red Hat OpenShift, Kubernetes, Docker, Terraform, or Bicep/ARM.
• Networking & Security: Knowledge of BGP, SD-WAN, Zero Trust, OWASP for LLMs, firewall management, RBAC, SIEM, and data privacy controls (encryption, tokenization).
• Software Engineering: Strong foundations in modular code design, testing, version control (Git), and automated CI/CD pipelines.