About the RoleWe are looking for an experienced AI/ML Architect to lead the design and implementation of advanced Generative AI and RAG (Retrieval-Augmented Generation) solutions. The role combines hands-on architecture design, pre-sales engagement, and technical leadership across enterprise AI initiatives.You will drive solutioning around LLMs, knowledge retrieval, and MCP-based multi-agent architectures, helping customers unlock business value from AI responsibly and at scale.Key Responsibilities- Architect and deliver enterprise-grade AI/ML & Generative AI solutions, including RAG pipelines, LLM integrations, and intelligent agents. - Engage in pre-sales activities: collaborate with business development, present technical solutions, estimate effort, and support proposals/PoCs for prospects. - Design knowledge retrieval layers using vector databases (FAISS, Pinecone, Milvus, Chroma, Weaviate). - Develop document ingestion, embedding, and context-retrieval pipelines for unstructured and structured data. - Architect and manage MCP (Model Context Protocol) servers for secure context exchange, multi-model orchestration, and agent-to-agent collaboration. - Define LLMOps / MLOps best practices – CI/CD for models, prompt versioning, monitoring, and automated evaluation. - Collaborate with pre-sales and business teams to shape AI solution proposals, PoCs, and client demos. - Lead AI innovation initiatives and mentor technical teams on GenAI, RAG, and MCP frameworks. - Ensure data privacy, compliance, and responsible AI across all deployments - Work closely with ITS, TIC team to provide mentorship and guidance to AI developersRequired Skills & Experience- 12–15 years of overall experience with 5–7 years in AI/ML and 3+ years in Generative AI / LLM architecture. - Strong hands-on experience with RAG pipelines, vector search, and semantic retrieval. - Proven experience integrating LLMs (OpenAI, Claude, Gemini, Mistral, etc.) using frameworks such as LangChain, LlamaIndex, or PromptFlow. - Deep understanding of MCP servers – configuration, context routing, memory management, and protocol-based interoperability. - Strong programming skills in Python, and familiarity with containerization (Docker, Kubernetes) and cloud AI services (Azure OpenAI, AWS Bedrock, GCP Vertex AI). - Expertise in MLOps/LLMOps tools (MLflow, KubeFlow, LangSmith, Weights & Biases). - Solid grounding in data engineering, pipelines, and orchestration tools (Airflow, Prefect). - Excellent communication, client engagement, and technical presentation skills - Proven track record of practice building or leadership in emerging technology domains.Preferred / Good to Have- Experience integrating MCP servers with LangChain agents or OpenAI’s MCP ecosystem for scalable orchestration. - Knowledge of RAG evaluation frameworks (RAGAS, TruLens) and hallucination-reduction techniques. - Experience with enterprise data connectors (SharePoint, Confluence, SQL/NoSQL, APIs). - Familiarity with Knowledge Graphs and hybrid retrieval (symbolic + neural). - Exposure to agentic frameworks like LangGraph, CrewAI, or AutoGen. - Proven experience building or scaling AI/GenAI Centers of Excellence (CoEs).Why Join Dexian- Be part of a growing Enterprise AI practice shaping next-generation intelligent systems. - Opportunity to architect cutting-edge GenAI and RAG solutions for global clients. - Collaborative and innovation-driven culture with deep technical mentorship.
Job Title
Artificial Intelligence Architect