Job description:

AI Systems ArchitectAn AI Systems Architect plays a critical role in designing and overseeing the end-to-end architecture of AI-driven systems. This role blends deep technical expertise in AI/ML with strong systems design, software engineering, and cloud infrastructure knowledge. Below are the key skills grouped by category:Core Technical SkillsAI/ML FundamentalsMachine learning algorithms (supervised, unsupervised, reinforcement learning)Deep learning (CNNs, RNNs, Transformers)Generative AI (LLMs, GANs, diffusion models)Model EngineeringModel selection, training, fine-tuning, evaluationPrompt engineering & retrieval-augmented generation (RAG)Knowledge of popular frameworks (TensorFlow, PyTorch, Hugging Face, LangChain)System DesignEnd-to-end architecture for scalable AI systemsReal-time inference and batch processing pipelinesData flow and orchestration (e.g., using Apache Airflow, Kubeflow)Data EngineeringData ingestion, preprocessing, and transformationData lakes and warehouses (Delta Lake, Snowflake, BigQuery)Feature engineering pipelinesSoftware EngineeringMicroservices and API designPython, Java, or Scala proficiencyCI/CD pipelines (GitHub Actions, Jenkins, etc.)Cloud & InfrastructureCloud AI platforms (AWS Sagemaker, Azure ML, Google Vertex AI)Kubernetes, Docker, and ML model deployment at scaleMonitoring and observability tools (Prometheus, Grafana, MLFlow)

• Key Responsibilities
  1. To assess the domain IT landscape assessment and Application portfolio optimization for gap analysis
  2. Creation of solution and architectural views (logical| conceptual| development| execution| infrastructure & operations architecture)
  3. To study and define system requirements addressing stakeholder| portfolio concerns
  4. To ensure knowledge up-gradation and work with new and emerging products/technologies
  5. To manage Non Functional Requirement adaption for the solution
  6. To contribute towards white/technical papers and knowledge base