AI Platform Architect

Company : Symphonyai

Job Type : Full Time

Albany

Number of Applicants

000+

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Job Description - AI Platform Architect

Job Description

We are seeking an experienced AI Architect to design, govern, and scale end-to-end AI solutions that deliver measurable business outcomes. This role sits at the intersection of data, machine learning, engineering, and product, translating business needs into robust, secure, and scalable AI architectures.

The AI Architect will define reference architectures, select platforms and tools, and guide teams in building production-grade AI systems across the enterprise.

Key Responsibilities

Platform Architecture & Vision

Own the end-to-end architecture for the AI platform, spanning:
- Agent frameworks and orchestration layers
- Semantic and knowledge graph foundations
- Data and signal ingestion fabric
- Model, reasoning, and tool-execution services
- Product and solution enablement layers
Establish modular, extensible reference architectures enabling rapid product and solution development.
Drive architectural consistency across teams building on AI Platform.

2.Agentic & Knowledge-Driven AI Systems

Architect agent-based systems capable of reasoning, planning, retrieval, and execution across enterprise workflows.
Design hybrid AI architectures combining:
- LLMs and multi-model stacks
- Knowledge graphs and ontologies
- Vector retrieval and semantic search
- Deterministic services and enterprise APIs
Lead the evolution of CINDE's semantic layer and retail knowledge foundation.

3. Solution Architecture & Business Enablement

Partner with Product, Engineering, and Business leaders to translate strategy into scalable technical systems.
Architect AI solutions across retail and CPG domains, including:
- Forecasting, demand intelligence, and optimization
- Price, promotion, and assortment intelligence
- Shopper personalization and retail media
- Store, shelf, and inventory intelligence
- Enterprise revenue and decision automation
Ensure architectures directly support revenue growth, product velocity, operational efficiency, and customer impact.

4. AI Platform Engineering, MLOps & LLMOps

Define CINDE standards for:
- Model lifecycle management
- Agent deployment and orchestration
- Prompt, workflow, and tool governance
- Experimentation and evaluation pipelines
Design scalable MLOps / LLMOps / AgentOps foundations:
- CI/CD for AI and agent workflows
- Observability, telemetry, and quality measurement
- Versioning, monitoring, drift detection, and retraining

5. Governance, Security & Responsible AI

Embed enterprise-grade security, privacy, and compliance into CINDE architecture.
Define and enforce Responsible AI frameworks across the platform:
- Explainability, traceability, and auditability
- Bias, safety, and risk controls
- Regulatory and customer-facing compliance readiness
Partner closely with Security, Legal, and Compliance leaders.

6. Technical Leadership & Influence

Serve as a technical north star across product and engineering organizations.
Mentor senior engineers, architects, and data scientists.
Influence platform decisions across multiple business units without direct authority.
Continuously assess emerging technologies and translate them into advantage.

Required Technical Skills

Cloud & Platform Engineering

Deep experience with AWS, Azure, or GCP AI platforms
Kubernetes, containerized AI workloads, and distributed systems
Infrastructure as Code and environment automation

Data, Knowledge & Signal Fabric

Enterprise data lakes and lakehouse platforms
Streaming and real-time signal architectures
Strong distributed data processing background
Knowledge graph platforms, semantic modeling, and ontologies

AI, ML & Agentic Systems

Expert-level Python
Production ML frameworks (PyTorch, TensorFlow, scikit-learn)
Agent frameworks and orchestration platforms
Multi-model system design

GenAI & Knowledge-Grounded AI

Commercial and open-source LLM ecosystems
RAG and hybrid retrieval architectures
Vector databases and embedding systems
Fine-tuning, evaluation, and prompt lifecycle managemen

MLOps / LLMOps / AgentOps

MLflow, Kubeflow, or equivalent platforms
CI/CD for AI workloads
Model and agent observability, testing, and governance

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AI Platform Architect

Job Description - AI Platform Architect

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