AI/ML Solutions Architect Generative AI

Job DescriptionWe are seeking a highly experienced AI/ML Solutions Architect with Generative AI expertise to lead the design and implementation of enterprise-scale AI solutions. The consultant will be responsible for architecting scalable AI/ML platforms, implementing LLM-based applications, and providing technical leadership across cross-functional teams.The ideal candidate must have strong hands-on experience in GenAI frameworks, cloud-native AI deployments, and enterprise architecture. Key ResponsibilitiesArchitect end-to-end AI/ML and Generative AI solutions aligned with enterprise standards.Design and implement LLM-powered applications (RAG, fine-tuning, embeddings, AI agents).Develop scalable ML pipelines including model training, validation, deployment, and monitoring.Lead cloud-native AI solution design on AWS / Azure / GCP.Design vector database integrations (Pinecone, FAISS, Weaviate, etc.).Implement MLOps best practices including CI/CD, model versioning, and monitoring.Provide architecture documentation (HLD/LLD), security controls, and governance standards.Partner with business stakeholders to translate requirements into AI technical designs.Support technical discussions during client workshops and architecture reviews. Required Qualifications10+ years of IT experience with 5+ years in AI/ML architecture.Strong experience in:o Python, ML frameworks (PyTorch, TensorFlow, Scikit-learn)o Generative AI & LLMso RAG architecture, embeddings, prompt engineeringo Vector databaseso REST APIs & MicroservicesHands-on cloud experience (AWS SageMaker / Azure ML / GCP Vertex AI).Experience with Docker, Kubernetes, and containerized deployments.Deep understanding of data engineering pipelines.Strong communication and client-facing experience. Preferred QualificationsExperience integrating OpenAI, Anthropic, or enterprise LLM platforms.Knowledge of AI governance, compliance, and responsible AI frameworks.Experience in regulated industries (BFSI, Healthcare).Exposure to AI security, data privacy, and model risk frameworks

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