AI Solutions Architect

We are seeking a highly experienced Generative AI Solutions Architect to lead the design and development of enterprise AI platforms, with a strong focus on agentic workflows and large language model applications. This role will be responsible for defining architecture standards, enabling scalable GenAI solutions, and supporting complex use cases across multiple business units., Architect and design scalable AI platform solutions across both new and existing environments, with a focus on Generative AI and agentic workflows

Lead the development and deployment of AI agents using frameworks such as LangChain and LangGraph

Design and implement Model Context Protocol (MCP) based architectures to enable dynamic tool and data integration for agentic applications

Own end to end architecture for GenAI use cases, including document processing and summarization across multiple data modalities such as text, images, and tables

Ensure production ready solutions by establishing best practices around accuracy, bias mitigation, hallucination reduction, PII handling, and guardrails

Evaluate and define key architectural components such as model selection, retrieval strategies, orchestration layers, and governance frameworks

Partner with business teams to enable agentic applications across the organization, defining how agents are designed, accessed, and scaled across use cases

Support deployment and integration within enterprise ML platforms, ensuring solutions are robust, secure, and production-ready

8 to 10+ years of experience in software engineering, data engineering, or AI/ML

Proven experience architecting enterprise-level AI or ML platforms

Strong expertise in Generative AI and LLM-based applications, including summarization and multi-modal workflows

Hands-on experience building and deploying agentic AI workflows, including MCP-based architectures

Strong Python programming skills, with ability to complete live coding assessments

Experience with machine learning projects including time series analysis, sentiment analysis, and topic modeling

Experience with AWS and AWS-certified preferred

Strong experience with PySpark, Spark, FastAPI, Kubernetes, and cloud deployments Preferred Qualifications:

Background as a hybrid data scientist and data engineer, with recent focus on LLM-driven solutions

Experience with vector databases such as Milvus to support embeddings and RAG architecture