Technical Architect

Requirements

• Design end-to-end software solutions covering frontend, backend, middleware, and integration layers. 
• Define and recommend technology stacks, frameworks, platforms, and tools aligned with project and organizational needs. 
• Design and implement microservices architectures with service discovery, fault tolerance, load balancing, and inter-service communication patterns. 
• Architect RESTful APIs, GraphQL endpoints, and event-driven interfaces for seamless internal and external integrations. 
• Leverage cloud platforms (AWS, Azure, GCP) to design scalable and secure infrastructure using compute, storage, networking, and IAM services. 
• Utilize Docker, Kubernetes, and container orchestration for scalable, continuous deployment-ready systems. 
• Design robust data storage solutions: 
• RDBMS: MySQL, PostgreSQL, Oracle 
• NoSQL: MongoDB, Cassandra, DynamoDB 
• Caching: Redis, Memcached 
• Search: Elasticsearch, Solr 
• Implement asynchronous communication using Kafka, RabbitMQ, or ActiveMQ.\ 
• Work with DevOps to build CI/CD pipelines and infrastructure-as-code (Terraform, CloudFormation, Ansible). 
• Conduct performance tuning, caching strategies, and load balancing to meet SLA targets. 
• Embed security best practices: secure coding, OAuth2, JWT, encryption, vulnerability scanning, OWASP compliance, GDPR readiness. 
  
• Implement monitoring, logging, and alerting using Prometheus, Grafana, ELK Stack, Splunk. 
• Perform regular code and architecture reviews to maintain standards and code quality. 
• Create and maintain architecture diagrams, design documentation, and technical specifications. 
• Lead proofs of concept (PoCs) and drive adoption of emerging technologies and frameworks. 
  

AI/ML & Deep Learning Responsibilities:

• Architect and implement AI/ML pipelines and deploy production-grade models using TensorFlow, PyTorch, Keras, ONNX, or JAX. 
• Deploy models using platforms like SageMaker, Vertex AI, Kubeflow, or Triton Inference Server. 
• Design deep learning systems for NLP, computer vision, forecasting, or speech processing using modern architectures (Transformers, CNNs, LSTMs, GANs). 
• Integrate MLOps practices: model versioning, continuous training, drift detection, and automated deployment. 
• Ensure model explainability (e.g., LIME, SHAP), performance optimization, and compliance with ethical AI standards. 
  

Required Skills:

• Programming: Python, Java, C#, Go, JavaScript/TypeScript 
• Architecture patterns: Microservices, Event-driven, SOA, Layered 
• Cloud: Deep experience with AWS, Azure, or GCP (including serverless and managed services) 
• Containers & orchestration: Docker, Kubernetes, Helm, Istio, Linkerd 
• Data: RDBMS (MySQL, Oracle, PostgreSQL), NoSQL (MongoDB, Cassandra), Redis, Elasticsearch 
• Messaging/Event systems: Kafka, RabbitMQ, ActiveMQ 
• API design & security: REST, GraphQL, Apigee, Kong, OAuth2, JWT, SAML 
• DevOps: CI/CD tools (Jenkins, GitLab CI, CircleCI), IaC (Terraform, CloudFormation) 
• Observability: Prometheus, Grafana, ELK Stack, Splunk 
• AI/ML: TensorFlow, PyTorch, MLflow, SageMaker, Kubeflow, ONNX, Triton