AI Architect
About Us:
We are redefining real-time consumer and audience insights. We partner with leading media companies and advertisers to bridge the gap between legacy research and modern measurement. Built by a team of industry veterans, we combine cutting-edge AI with deep market intelligence to provide media organizations, brands, and agencies with data that is accurate, actionable, and immediate. Our technology transforms fragmented and delayed insights into streamlined, always-on solutions - empowering companies to make smarter, faster decisions at scale. Whether it''s national or hyper-local data, our AI-driven insights deliver a clear, competitive advantage.
Position Overview:
Lead the design and implementation of core AI and LLM capabilities— including RAG pipelines, embeddings, vector search, and AI agents—while building reusable services that teams can adopt across the platform. Drive AI-assisted engineering enablement, apply LLMs to data and automation use cases, and establish practical MLOps/LLMOps practices. Partner closely with engineering, product, data, and security to identify high-impact AI opportunities, scale AI-integrated features, and ensure responsible, safe use of AI across the organization.
Key Responsibilities:
AI Architecture & Development
- Design and implement core AI/LLM capabilities, including RAG pipelines, embeddings, vector search, and agent-based workflows.
- Build reusable AI components and services that engineering and data teams can adopt across the platform.
- Prototype, evaluate, and refine new AI models, tools, and approaches.
AI-Assisted Engineering Enablement
- Lead the adoption of AI-assisted engineering tools (e.g., GitHub Copilot Enterprise, Cursor, Copilot Workspace) across the development team.
- Define practical, safe ways engineers can use AI for coding, testing, refactoring, documentation, and problem-solving.
- Support engineers in integrating AI into their daily workflows.
AI for Data & Automation
- Use LLMs and agents to streamline data processing tasks such as cleaning, classification, enrichment, and validation.
- Build internal automation tools and AI copilots that accelerate engineering, analytics, and operational workflows.
MLOps / LLMOps (Practical, not Enterprise-Scale)
- Implement evaluation, monitoring, and versioning practices for prompts, models, and RAG configurations.
- Partner with DevOps/SRE to integrate AI workloads into existing CI/CD and monitoring systems.
Collaboration & Cross-Functional Support
- Work closely with engineering, product, and data teams to identify where AI can add value and deliver solutions.
- Contribute architectural guidance on how to build, deploy, and scale AI-integrated features.
- Document and share best practices to help teams adopt AI responsibly and effectively.
Responsible & Safe AI Usage
- Help establish guardrails for safe AI tool usage within engineering and internal workflows.
- Ensure AI systems meet privacy, security, and data handling standards in partnership with security/compliance.
Qualifications:
Required
- 6–10+ years in AI/ML engineering, software engineering, or data engineering, with strong recent LLM experience.
- Hands-on experience building LLM/RAG solutions (embeddings, vector DBs, retrieval pipelines).
- Strong Python skills and experience with modern AI toolchains (OpenAI, Anthropic, HuggingFace, LangChain/LlamaIndex).
- Practical experience with AI-assisted engineering tools (Copilot Enterprise, Cursor, Copilot Workspace, or similar).
- Solid understanding of ML fundamentals: evaluation, optimization, quality checks.
- Experience using AI/LLMs for data transformation tasks (cleaning, classification, enrichment).
- Familiarity with MLOps/LLMOps concepts (versioning, testing, monitoring), even if not at massive scale.
- Ability to work cross-functionally and explain technical concepts to engineering and product partners.
- Comfortable operating autonomously and iterating quickly in a fast-moving environment.
Preferred:
- Experience integrating AI into CI/CD or automating DevOps workflows.
- Experience with TensorFlow or PyTorch (nice-to-have, not required).
- Exposure to AI governance, safe-use practices, and compliance considerations.
- Experience working in SaaS, analytics, or data-driven environments.
- Bachelor’s degree in CS, Engineering, Data Science, or equivalent experience.
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