AI Architect

Sonata Software Overview In today's market, there is a unique duality in technology adoption. On one side, extreme focus on cost containment by clients, and on the other, deep motivation to modernize their Digital storefronts to attract more consumers and B2B customers. As a leading Modernization Engineering company, we aim to deliver modernization-driven hypergrowth for our clients based on the deep differentiation we have created in Modernization Engineering, powered by our Lightening suite and 16-step Platformation™ playbook. Headquartered in Bengaluru, India, Sonata has a strong global presence, including key regions in the US, UK, Europe, APAC, and ANZ. We are a trusted partner of world-leading companies in BFSI (Banking, Financial Services, and Insurance), HLS (Healthcare and Lifesciences), TMT (Telecom, Media, and Technology), Retail & CPG, and Manufacturing space. Our bouquet of Modernization Engineering Services cuts across Cloud, Data, Dynamics, Contact Centers, and around newer technologies like Generative AI, MS Fabric, and other modernization platforms. The Generative AI Architect is responsible for defining, designing, and governing end-to-end GenAI solution architectures across business functions and delivery projects. The role involves working with data, AI, and application teams to conceptualize and deliver AI-powered copilots, agents, and automation platforms that enhance productivity, decision-making, and customer experience. This individual will drive architecture blueprints , reference implementations , and governance frameworks for the enterprise GenAI ecosystem — ensuring scalability, compliance, security, and business value realization. Solution Architecture & Design Define enterprise-level GenAI reference architectures encompassing LLMs , RAG pipelines , vector databases , and integration layers . Design and oversee Copilot, AI Agent, and Chatbot architectures leveraging frameworks like Azure AI Studio , OpenAI APIs , LangChain , Semantic Kernel , or AWS Bedrock . Architect data grounding strategies , prompt orchestration , context management , and hybrid retrieval flows . based on use case, compliance, and performance. Microsoft 365 Copilot , Dynamics 365 , SAP , Salesforce , custom apps ). Architect multi-modal AI solutions using text, image, audio, and document inputs. Collaborate with Data Engineering and MLOps teams to operationalize AI pipelines using Azure ML , SageMaker , or Vertex AI . Design API-first architectures and AI microservices for reusable components. Establish and enforce Responsible AI guidelines covering data privacy, hallucination management, prompt security , and model transparency . Define policies for model selection , fine-tuning , data retention , and safety evaluation . Partner with cybersecurity and legal teams to ensure adherence to organizational and regulatory frameworks (GDPR, SOC2, ISO 27001) . Performance Optimization & Monitoring Drive continuous improvement through experimentation, A/B testing, and cost-performance optimization. Collaborate with Product Owners , Solution Architects , Data Scientists , and Engineering Teams to deliver end-to-end GenAI initiatives. Provide technical leadership and mentorship to AI engineers and developers. Represent the organization in AI CoE and innovation forums to define strategic roadmaps. Required Skills & Experience: ~10–18 years in IT, with strong experience in AI/ML architecture , cloud solutions , or enterprise applications . ~ Hands-on expertise in Generative AI frameworks and LLM orchestration (LangChain, Semantic Kernel, LlamaIndex, PromptFlow). ~ Proficiency with Azure OpenAI , OpenAI API , AWS Bedrock , or Google Vertex AI . ~ Strong understanding of RAG architecture , vector databases (Pinecone, Chroma, Weaviate, FAISS), and embedding models . ~ Proficiency in Python , C# , or TypeScript for AI integration and orchestration. ~ Deep familiarity with Azure , AWS , or GCP AI services and DevOps pipelines . ~ Experience designing secure, scalable, multi-tenant architectures for AI workloads. Experience integrating Copilot Studio , Power Platform , or Fabric AI with enterprise systems. Exposure to enterprise data ecosystems (Data Lake, Synapse, Snowflake, Databricks). Knowledge of knowledge graphs , vector retrieval optimization , or multi-agent AI systems . Familiarity with cost governance , AI model licensing , and fine-tuning frameworks (LoRA, PEFT, RAG-Fusion) . Microsoft / AWS / Google AI certifications. Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Engineering, or related fields. Why join Sonata Software