Job Description

About the Role

Carrier is seeking a highly skilled and forward‑thinking AI & Data Solution Architect to lead the design, architecture, and delivery of enterprise‑scale Data, Analytics, and AI solutions across the organization. This role will play a critical part in defining enterprise data and AI architecture standards, shaping cloud‑first platforms, and enabling scalable, secure, and cost‑efficient adoption of analytics, machine learning, and generative AI.

The ideal candidate brings deep expertise across GCP, AWS, and Snowflake, strong experience with modern data platforms and lakehouse architectures, and proven leadership in AI/ML, Generative AI, and MLOps/LLMOps initiatives in production environments.

Key Responsibilities

Strategic & Architectural Leadership

• Define and evolve Carrier’s AI & Data architecture strategy and roadmap, aligned with business priorities and IT strategy.
• Serve as a thought leader for modern data, analytics, and AI architectures, including Generative AI and Agentic AI.
• Identify, evaluate, and recommend emerging technologies, platforms, and architectural patterns.
• Partner with business and digital leaders to identify and prioritize high‑impact AI and analytics use cases.
• Provide architectural guidance on ethical, responsible, and compliant AI adoption.

Solution Architecture & Platform Design

• Lead end‑to‑end architecture design for complex data, analytics, and AI initiatives, ensuring scalability, performance, security, and cost efficiency.

Design and govern cloud‑based data platforms leveraging:

• Google Cloud Platform (BigQuery, Vertex AI, Dataflow, Dataproc, Looker)
• AWS (S3, Glue, EMR, Redshift, SageMaker, Lambda)
• Snowflake (data warehouse, data sharing, performance optimization)

Architect modern enterprise data architectures, including:

• Data Lake, Lakehouse, Data Mesh, and Data Fabric
• Open table/file formats such as Parquet, Iceberg, Delta Lake
• Medallion architectures (Bronze/Silver/Gold)
• Define data ingestion and integration patterns across structured and semi‑structured sources (SAP, Oracle, Salesforce, JDE, Ariba, IoT, APIs, NoSQL).
• Define and enforce data quality, metadata, lineage, and access control standards.

AI, ML, and Generative AI Architecture

• Design and implement AI/ML and GenAI solution architectures from experimentation through production.
• Architect solutions for core ML use cases such as demand forecasting, predictive maintenance, supply chain optimization, and customer analytics.

Lead architecture for Generative AI and Agentic AI, including:

• LLM integration with tools, APIs, and knowledge bases (RAG patterns)
• Autonomous and semi‑autonomous agent workflows
• Fine‑tuning, prompt engineering, and optimization strategies
• Establish MLOps and LLMOps frameworks for model training, deployment, monitoring, evaluation, and lifecycle management.
• Define approaches for model observability, explainability (XAI), bias detection, and risk mitigation.

Technical Leadership & Collaboration

• Provide technical leadership and mentorship to solution architects, data engineers, data scientists, and AI engineers.
• Collaborate closely with platform, DevOps, and cloud engineering teams to enable automation‑driven deployments.
• Review solution designs, conduct architecture assessments, and provide impact analysis and recommendations.
• Communicate complex technical concepts clearly to both technical and executive audiences.

Required Qualifications

• Bachelor’s Degree in engineering or technical discipline.
• 8+ years hands‑on experience in data architecture, analytics solutions, and/or cloud data platforms.
• 3+ years of hands‑on experience delivering AI/ML and Generative AI solutions in production.
• 6+ years of experience designing and scaling enterprise data platforms on GCP, AWS, and Snowflake.
• Must have unrestricted authorization to work in the USA. No visa sponsoring available.

Preferred Qualifications

• Master’s degree or Ph.D. preferred.
• Demonstrated success leading large‑scale, cross‑functional data and AI initiatives.
• Cloud platforms: GCP and AWS (multi‑cloud experience strongly preferred)
• Data platforms: Snowflake, BigQuery, Data Lakes, Lakehouse architectures
• Programming & analytics: Python, SQL, PySpark
• AI/ML frameworks (TensorFlow, PyTorch, scikit‑learn, XGBoost)
• GenAI/LLM frameworks, vector databases, graph databases
• Data engineering tools (Spark, Kafka, Hadoop)
• Containerization and orchestration (Docker, Kubernetes)
• CI/CD and DevOps practices
• Strong understanding of data modeling, performance tuning, and cost optimization.
• Strong architectural thinking and problem‑solving skills.
• Excellent communication and stakeholder management capabilities.
• Ability to influence without authority and operate effectively in matrixed organizations.
• Self‑driven, organized, and able to manage multiple priorities.

Preferred Certifications

• AWS Certified Solutions Architect
• Google Cloud Professional Cloud Architect
• Snowflake or Data Engineering certifications