AI Platform Engineer (Architecture & Security)

Position Summary

As an AI Platform Engineer, you will be responsible for designing, securing, and scaling enterprise-grade AI platforms and end-to-end Machine Learning workflows. Sitting at the intersection of system architecture, security engineering, and MLOps, this role ensures our central AI ecosystem is secureby-design, scalable, and business-aligned. You will architect robust end-to-end pipelines-ranging from agentic AI workflows, Retrieval-Augmented Generation (RAG) services, and LLM runtime environments to data ingestion and API orchestration. Concurrently, you will embed cybersecurity controls, privacy-preserving mechanisms, Responsible AI (RAI) frameworks, and compliance policies into every layer of our hybrid cloud and on-premise AI infrastructure.

Key Responsibilities

1. Platform Architecture & AI Workflow Design

• End-to-End Solution Architecture: Design and deploy enterprisegrade AI workflows spanning data pipelines, model orchestration, API integration, and serving layers across hybrid cloud and on-premise environments.

• Core AI Services & Runtime: Architect shared core services for the central AI platform, including Agentic AI frameworks, AI workbenches, Model Context Protocol (MCP), shared RAG capabilities, and AI runtime environments.

• Integration & Orchestration: Integrate AI systems with enterprise platforms and APIs, leveraging advanced orchestration tools (e.g., LangChain, LangGraph, vector databases).

• Standards & Reference Patterns: Define architectural blueprints, reusable design patterns, and reference implementations to streamline AI deployment across different business units.

2. AI Security, Risk & Governance

• Secure-by-Design Architecture: Embed security, data privacy, and compliance principles into AI platforms, data pipelines, and deployment frameworks.

• Threat Modeling & Risk Assessments: Conduct AI-specific threat modeling and risk evaluations addressing model misuse, data leakage, prompt injection vulnerabilities, adversarial attacks, and LLM security.

• Data Privacy & Controls: Implement privacy-preserving techniques into AI workflows, including data anonymization, tokenization, encryption in transit/at rest, role-based access controls (RBAC), and secure logging.

• Responsible & Explainable AI (RAI/XAI): Establish frameworks for Responsible AI and Explainable AI to ensure model decisions remain transparent, interpretable, ethical, and aligned with governance policies.

3. Engineering Operations & Platform Strategy

• Tooling Evaluation: Evaluate, recommend, and integrate platform and security toolkits (e.g., Databricks, AWS Guardrails, Azure Responsible AI, open-source AI frameworks).

• Vulnerability & Testing Oversight: Coordinate and approve Vulnerability Assessment and Penetration Testing (VAPT) for both inhouse models and third-party/open-source AI integrations.

• Prototyping & Leadership: Drive technical Proofs of Concept (PoCs), vendor technology reviews, and innovation pilots while guiding small engineering teams through implementation.

Skills for Success

Qualifications & Experience

• Bachelor's or Master's degree in Computer Science, Cybersecurity, Engineering, Data Science, AI/ML, or a related technical discipline.

• 3-5+ years of experience in enterprise architecture, cybersecurity, secure systems engineering, or AI/ML platform integration.

• Certifications (Advantageous): Certifications in major cloud platforms (AWS, Azure, GCP), Databricks, or Security Architecture.

Technical Skills

• AI/ML Architecture & Frameworks: Hands-on experience with AI pipeline design, model serving APIs, vector databases, agentic frameworks, and LLM orchestration (e.g., LangChain, LangGraph, MCP).

• Cloud & Platform Ecosystems: Proficiency with major cloud AI platforms (Azure ML, AWS SageMaker, GCP Vertex AI) and big data/container engines.

• AI Security & Governance: Knowledge of OWASP Top 10 for LLMs, Zero Trust frameworks, DevSecOps practices, RBAC, and secrets management.

• Data Protection & Compliance: Strong technical understanding of data privacy controls (encryption, tokenization, anonymization) and regulatory compliance requirements.