- Company Name
- Viral Nation
- Job Title
- AI Integration Engineer, AI/ML
- Job Description
-
Job Title: AI Integration Engineer, AI/ML
Role Summary: Primary technical lead translating high‑level AI/ML architectures into production‑ready systems within the Google Cloud Platform ecosystem. Responsible for end‑to‑end development, integration, and deployment of AI models, agent workflows, and data pipelines for influencer‑marketing applications.
Expectations: • Deliver scalable, maintainable code that meets design reference architectures. • Maintain high model performance, ethical standards, and cost efficiency. • Collaborate with Principal Architect and VP of Engineering to align engineering practices with strategic objectives. • Own continuous delivery, monitoring, and iterative improvement of AI agents and pipelines.
Key Responsibilities:
- Convert architected reference models into production code using Python, GKE, Cloud Run, and Vertex AI Pipelines.
- Build and orchestrate agent ecosystems with Google ADK and Model Context Protocol (MCP), implementing Agent‑to‑Agent (A2A) patterns.
- Design and optimize prompt and context strategies (Chain‑of‑Thought, ReAct) to maximize LLM reasoning and statefulness.
- Develop high‑fidelity Retrieval‑Augmented Generation (RAG) pipelines with Google RAG Engine for influencer‑marketing workflows.
- Implement automated anomaly detection, forecasting, and trend analysis on large‑scale datasets using classical ML techniques.
- Generate, query, and analyze high‑dimensional video and text embeddings for semantic search and insights extraction.
- Integrate Gemini and Model Garden LLMs into production, managing token limits and context windows.
- Apply MLOps practices: versioning, monitoring, CI/CD via GitHub Actions, and IAM‑driven governance for performance, cost, and ethical safeguards.
- Collaborate with front‑end teams (Node.js) and data teams (Snowflake, Cortex) as needed.
Required Skills:
- Advanced Python programming with strong API, data‑analysis, and backend development experience.
- Deep working knowledge of GCP services: GKE, Cloud Run, Vertex AI, RAG Engine, ADK, MCP.
- Expertise in prompt engineering (Chain‑of‑Thought, ReAct) and context engineering for LLMs.
- Hands‑on experience with vector databases, embedding manipulation, and semantic search.
- Proficiency in classic ML for regression, forecasting, anomaly detection on large datasets.
- Familiarity with Docker, Kubernetes (GKE), and CI/CD pipelines (GitHub Actions).
- Understanding of MLOps principles, monitoring, cost‑tracking, and ethical AI governance.
- Strong communication skills and ability to work cross‑functionally with architects and product teams.
Required Education & Certifications:
- Bachelor’s degree in Computer Science, Data Science, Electrical Engineering, or related quantitative field (or equivalent professional experience).
- Google Cloud certifications (Professional Data Engineer, Professional Machine Learning Engineer) preferred.