As a Solutions Architect, you will lead the design and implementation of enterprise-grade Generative AI solutions.

You will bridge the gap between complex data ecosystems and cutting-edge LLM applications, ensuring scalability, security, and measurable ROI.

Key Responsibilities :

- Architectural Leadership Design end-to-end Gen AI architectures, including RAG (Retrieval-Augmented Generation) frameworks, Agentic workflows, and Multi-modal systems.

- Data Backbone Build and oversee robust data pipelines that feed AI models, leveraging your background in Big Data and Cloud Data Warehousing.

- Model Lifecycle Manage LLM selection, prompt engineering, fine-tuning strategies, and deployment using MLOps/LLMOps best practices.

- Enterprise Integration Ensure AI solutions integrate seamlessly with existing enterprise APIs, microservices, and security protocols.

- Stakeholder Management Translate complex technical AI concepts into business value for C-suite stakeholders.

Technical Skills Required :

- Gen AI LangChain, LlamaIndex, OpenAI/Anthropic SDKs, AgenticAI, Vector DBs (Milvus, Pinecone, Weaviate).

- Data Engineering Apache Spark, Python, SQL, ETL/ELT, Databricks, or Snowflake.

- Cloud AWS (Bedrock/SageMaker), Azure (OpenAI Service), or GCP (Vertex AI).

- DevOps Docker, Kubernetes, and CI/CD for AI.

- Only Immediate joiners.