AI Architect

SymphonyAI•City of Albany, NY

About The Position

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.

Requirements

8–15+ years of experience in software engineering, data engineering, or architecture
5+ years designing and deploying AI/ML systems in production
Strong understanding of enterprise architecture patterns
Proven ability to influence senior technical and business stakeholders
Experience operating in complex, cross-functional environments
Cloud & Infrastructure: AWS (SageMaker, Bedrock), Azure (Azure ML, OpenAI), or GCP (Vertex AI)
Kubernetes, Docker
Infrastructure as Code (Terraform, CloudFormation)
Data & Analytics Platforms: Data lakes / lakehouse: Databricks, Snowflake, BigQuery, Redshift
Streaming & ingestion: Kafka, Kinesis, Pub/Sub
SQL and distributed data processing (Spark)
Machine Learning & AI: Python (required)
ML frameworks: TensorFlow, PyTorch, scikit-learn
Model development and experimentation tools
Feature stores (Feast, Databricks Feature Store)
GenAI & NLP: LLMs: OpenAI, Anthropic, Google, open-source (LLaMA, Mistral)
Prompt engineering and orchestration frameworks (LangChain, LlamaIndex)
Vector databases (Pinecone, Weaviate, FAISS)
RAG architectures and fine-tuning approaches
MLOps / LLMOps: MLflow, SageMaker Pipelines, Kubeflow
Model monitoring and observability tools
CI/CD integration for AI workloads
Governance & Security: Model explainability tools (SHAP, LIME)
Data privacy and access controls
AI risk and compliance frameworks

Nice To Haves

Retail, supply chain, commerce, SaaS, or large-scale enterprise environments
Experience scaling AI beyond pilots into enterprise platforms
Exposure to global or multi-region deployments

Responsibilities

Define and own enterprise AI architecture spanning data ingestion, model development, deployment, and monitoring
Design scalable architectures for ML, GenAI, and advanced analytics use cases
Establish standards, best practices, and guardrails for AI development and deployment
Drive build vs. buy decisions for AI platforms, tools, and vendors
Partner with Product, Engineering, Data Science, and Business teams to translate requirements into technical solutions
Architect AI solutions for use cases such as: Forecasting and optimization Personalization and recommendations Computer vision Natural language processing and GenAI
Ensure solutions meet performance, reliability, security, and compliance requirements
Design and govern MLOps / LLMOps pipelines to support model lifecycle management
Enable CI/CD for models, prompt management, and experimentation
Guide teams on model versioning, monitoring, retraining, and drift detection
Ensure compliance with data privacy, security, and regulatory requirements
Define and enforce Responsible AI practices (fairness, explainability, auditability)
Partner with Security, Legal, and Compliance teams on AI risk management
Serve as a technical mentor to engineers, data scientists, and architects
Influence architecture decisions across multiple teams without direct authority
Stay current with emerging AI technologies and assess applicability to the business