AI Technical Architect

Job Description:

Key Responsibilities

Strategic Planning & Architecture

• 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.

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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.

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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.

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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.

Stakeholder Engagement & Communication

• 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.

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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.

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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)