Kaleris
Job Description:Key ResponsibilitiesStrategic Planning & ArchitectureDevelop 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 & DeliveryLead 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 LeadershipLead 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 GovernanceOversee 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.Stakeholder Engagement & CommunicationCollaborate 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 FocusAdopt 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 RequiredBachelor'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 scaleDemonstrated expertise in Azure, AWS, and AI/ML platforms (TensorFlow, PyTorch, Hugging Face, etc.)Advanced hands-on experience with MCP server setup, optimization, and troubleshootingData 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 securityPractical knowledge of API/microservice architectures and API security best practicesExperience integrating AI with IoT and edge architecturesStrong project management, team leadership, and stakeholder communication skillsModel 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 robustnessStrong background in cloud performance & cost optimization and multi-cloud resiliency PreferredAdvanced certifications (Azure, AWS, AI/ML)Experience with regulatory frameworks (HIPAA, SOC2, FHIR, HL7, EDI)AI/LLM-specific certificationsFamiliarity with edge AI deployment accelerators (NVIDIA Jetson, AWS Greengrass, Azure Percept)Experience with hybrid and multi-cloud AI architecturesKaleris is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.