The Role
The AI Technical Architect will develop AI strategies, lead project management, ensure integration with existing systems, and oversee AI model deployment and optimization.
Summary Generated by Built In
Job Description:
- Develop future-ready AI strategies and technical roadmaps aligned with business goals.
- Identify and evaluate opportunities for AI to create value, leveraging emerging trends (e.g., neural networks, quantum computing, hybrid models).
- Architect solutions incorporating AI/ML (including hybrid models), big data, and integration with IoT and edge computing to support real-time and low-latency business needs.
Solution Design, Integration & Delivery- Lead the architectural design, development, and deployment of reliable, scalable AI/ML systems across Azure, AWS, and hybrid clouds.
- Ensure seamless integration of AI with broader enterprise applications, cloud infrastructures, MCP servers, databases, and IoT/edge devices.
- Select and integrate appropriate tools, platforms, and industry-standard frameworks (e.g., TensorFlow, PyTorch, Hugging Face, Hadoop, Spark, Kafka).
- Expose and secure AI/ML models via REST/gRPC interfaces deployed in microservices architectures; manage API gateways, load balancing, and API security.
Project & Team Leadership- Lead AI project operations—scoping, planning, and driving projects to completion on time and budget.
- Mentor and develop AI technical teams; facilitate knowledge transfer on best practices, new technologies, and responsible AI use.
- Foster a culture of innovation, transparency, collaboration, accountability, and ethical AI.
- Champion responsible AI: Promote justice, transparency, and accountability in AI development and deployment.
Evaluation, Optimization, and Governance- Oversee performance monitoring, optimization, and retraining of production AI systems.
- Implement and maintain robust MLOps pipelines (CI/CD, monitoring, automated retraining) and model management (versioning, rollback, and decommissioning).
- Leverage model registries such as MLflow, SageMaker Model Registry, or Azure ML Registry.
- Apply advanced observability and monitoring (Prometheus, Grafana, OpenTelemetry, DataDog, ELK stack).
- Ensure compliance with security, privacy, and regulatory (e.g., HIPAA, SOC2) and ethical AI standards.
- Apply privacy-preserving techniques (differential privacy, federated learning, data anonymization).
- Apply model validation, A/B testing, canary deployments, and adversarial testing for AI reliability.
- Collaborate with business stakeholders, data scientists, and engineers to translate organizational/business needs into actionable AI solutions.
- Clearly articulate AI system benefits, limitations, risks, and future possibilities to technical and non-technical audiences.
Future-Oriented Focus- Adopt Cutting-Edge AI: Evaluate and leverage new developments (neural networks, quantum, hybrid models).
- Drive Hybrid AI Models: Architect solutions combining machine learning, neural nets, and rule-based methods.
- Integrate AI with IoT/Edge: Deploy AI for IoT and edge scenarios (e.g., NVIDIA Jetson, AWS Greengrass, Azure Percept) for real-time, decentralized intelligence.
- ML/LLM Ops: Apply best practices in LLMOps, vector databases (Pinecone, ChromaDB, Weaviate), and prompt engineering for LLM-based solutions.
- Champion Responsible AI: Promote fairness, transparency, and ethical AI across all projects.
Qualifications Required- Bachelor’s in Computer Science, Engineering, or related field (Master’s preferred)
- 8+ years in technical/data/solution architecture roles, with 4+ years focused on AI/ML systems at enterprise scale
- Demonstrated expertise in Azure, AWS, and AI/ML platforms (TensorFlow, PyTorch, Hugging Face, etc.)
- Advanced hands-on experience with MCP server setup, optimization, and troubleshooting
- Data engineering proficiency (big data, ETL/ELT, Hadoop, Spark, Kafka, etc.)
- Expertise in AI model deployment, serving/inference frameworks (Triton, TensorRT, VLLM, TGI, etc.)
- Programming proficiency (Python, R, Java)
- Experience with DevOps, MLOps, and CI/CD for AI projects; Infrastructure-as-Code skills (Terraform, CloudFormation, ARM)
- Advanced skills in containerization/orchestration (Docker, Kubernetes) and container security
- Practical knowledge of API/microservice architectures and API security best practices
- Experience integrating AI with IoT and edge architectures
- Strong project management, team leadership, and stakeholder communication skills
- Model lifecycle management (development, versioning, monitoring, rollback, decommissioning)
- Experience with monitoring and observability tools for AI/ML workloads (Prometheus, Grafana, DataDog, OpenTelemetry)
- Familiarity with privacy-preserving ML techniques (differential privacy, federated learning)
- Experience and proficiency in model testing, validation, and adversarial robustness
- Strong background in cloud performance & cost optimization and multi-cloud resiliency
Preferred- Advanced certifications (Azure, AWS, AI/ML)
- Experience with regulatory frameworks (HIPAA, SOC2, FHIR, HL7, EDI)
- AI/LLM-specific certifications
- Familiarity with edge AI deployment accelerators (NVIDIA Jetson, AWS Greengrass, Azure Percept)
- Experience with hybrid and multi-cloud AI architectures
Kaleris is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.
Top Skills
Arm
AWS
Azure
CloudFormation
Datadog
Docker
Grafana
Hadoop
Hugging Face
Java
Kafka
Kubernetes
Mlflow
Opentelemetry
Prometheus
Python
PyTorch
R
Sagemaker Model Registry
Spark
TensorFlow
Terraform
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The Company
What We Do
Kaleris is a leading provider of cloud-based supply chain execution and visibility technology solutions. Many of the world's largest brands rely on Kaleris to provide mission-critical technology for yard management, transportation management, maintenance and repair operations, terminal operating systems, and ocean carrier and vessel solutions. By consolidating supply chain execution software assets across major nodes and modes, we address the dark spots and data gaps that cause friction and inefficiency in the global supply chain.
