- Company Name
- Neurealm
- Job Title
- Enterprise Solutions Architect (AI)
- Job Description
-
**Job Title:** Enterprise Solutions Architect (AI)
**Role Summary:**
Designs and delivers scalable, secure, enterprise‑grade data and AI solutions on Google Cloud Platform. Leads end‑to‑end architecture for data lakehouses, modern BI, MLOps, Generative AI, and Agentic AI, collaborating with business, data engineering, and AI teams to drive AI‑led transformation.
**Expectations:**
- Own architecture vision and implementation across data, AI/ML, and analytics domains.
- Align technical solutions with business objectives and governance standards.
- Provide technical leadership, mentorship, and documentation for cross‑functional teams.
- Ensure solutions are secure, cost‑optimized, and performant at enterprise scale.
**Key Responsibilities:**
- Design and govern end‑to‑end data architectures (ingestion, processing, storage, governance, consumption) on GCP.
- Build modern data lakehouse platforms using BigQuery, Dataproc, Cloud Storage, etc.
- Define and implement MLOps frameworks (CI/CD, feature stores, model registries, monitoring).
- Architect Generative AI and Agentic AI solutions leveraging LLMs and conversational analytics.
- Create AI‑powered BI platforms, integrating tools like Looker, Tableau, or Power BI with data layers.
- Lead PoCs, vendor assessments, and solution evaluations for GenAI and autonomous agents.
- Conduct technical design reviews, produce architecture blueprints, and maintain reference documentation.
- Partner with business leaders to translate requirements into AI‑driven solutions.
**Required Skills:**
- Deep expertise in data architecture and lakehouse design.
- Strong background in AI/ML architecture, MLOps, and model lifecycle management.
- Hands‑on experience with Generative AI, LLMs, and conversational analytics.
- Proficiency with Agentic AI frameworks and autonomous agent orchestration.
- Advanced knowledge of GCP services: BigQuery, Vertex AI, Dataflow, Dataproc, Pub/Sub, Looker, IAM.
- Programming: SQL, Python; big data: Spark, Kafka.
- BI tools: Looker, Tableau, Power BI (or equivalents).
- Familiarity with TensorFlow, PyTorch, scikit‑learn, and CI/CD pipelines.
- Strong communication, stakeholder management, and mentoring abilities.
**Required Education & Certifications:**
- Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or related field.
- Preferred: Google Cloud Professional certifications (Professional Data Engineer, Professional ML Engineer, Cloud Architect).
- Experience in large‑scale enterprise or consulting environments.