1. Job Title: AI/ML Architect Key Responsibilities: Design and review machine learning architecture pipelines end-to-end. Architect scalable models using tools like TensorFlow, PyTorch, Scikit-learn. Drive data strategy, feature stores, model lifecycle (training, deployment, monitoring). Collaborate with data engineering and product teams to align technical goals. Define standards for MLOps, model governance, and responsible AI. Must-Have Skills: 8+ years in ML/DL, data science, or AI-related roles. Expertise in Python, ML frameworks, distributed computing (Spark, Dask). Cloud-native ML deployment on AWS SageMaker, Azure ML, GCP Vertex AI. Knowledge of Kubeflow, MLFlow, Airflow, Docker/K8s for ML pipelines. Strong grasp of ML lifecycle management, monitoring, and retraining. 2. Job Title: Java Architect Key Responsibilities: Own the technical design for backend systems built on Java / Spring Boot. Create solution blueprints, architecture documents, and design patterns. Guide teams on microservices, containerization, API design, and DevOps. Conduct code reviews, architecture assessments, and risk mitigation. Collaborate with product and infrastructure teams for seamless delivery. Must-Have Skills: Proficiency in Java 11+/Spring Boot, RESTful APIs, ORM frameworks. Experience with Kubernetes, Docker, messaging systems (Kafka, RabbitMQ). Strong grasp of cloud-native development (AWS, Azure, GCP). In-depth understanding of microservices, DDD, clean architecture. Performance tuning, scalability, and reliability design. 3. Job Title: Solution Architect Key Responsibilities: Analyze requirements and design technical solutions across platforms. Create architecture roadmaps for applications, integrations, and cloud infrastructure. Lead POCs, architecture evaluations, and tech selection. Align solutions with enterprise architecture standards and security best practices. Act as a technical bridge between stakeholders, developers, and leadership. Must-Have Skills: Strong experience in application architecture, APIs, and system integration. Familiarity with cloud platforms (Azure, AWS, GCP). Solid understanding of data architecture, DevOps practices, CI/CD pipelines. Experience with design patterns, scalability, and resilience planning. Great communication, documentation, and stakeholder management skills.