Job
Description
Role Overview: As an experienced AI/ML Architect, you will be responsible for leading the design and implementation of advanced Generative AI and RAG solutions at the enterprise level. This role will involve a combination of hands-on architecture design, pre-sales engagement, and technical leadership to drive AI initiatives that deliver business value responsibly and at scale. You will play a crucial role in designing and delivering enterprise-grade AI/ML solutions, engaging in pre-sales activities, and shaping AI solution proposals for clients. 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 by collaborating with business development, presenting technical solutions, estimating effort, and supporting proposals/PoCs for prospects. - Design knowledge retrieval layers using vector databases such as FAISS, Pinecone, Milvus, Chroma, and Weaviate. - Develop document ingestion, embedding, and context-retrieval pipelines for both unstructured and structured data. - Architect and manage MCP servers for secure context exchange, multi-model orchestration, and agent-to-agent collaboration. - Define LLMOps / MLOps best practices including 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 and TIC team to provide mentorship and guidance to AI developers. Qualification Required: - 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 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, familiarity with containerization (Docker, Kubernetes), and cloud AI services (Azure OpenAI, AWS Bedrock, GCP Vertex AI). - Expertise in MLOps/LLMOps tools such as MLflow, KubeFlow, LangSmith, Weights & Biases. - Solid grounding in data engineering, pipelines, and orchestration tools like Airflow and Prefect. - Excellent communication, client engagement, and technical presentation skills. - Proven track record of practice building or leadership in emerging technology domains. Additional Company Details (if present): Join Dexian to be part of a growing Enterprise AI practice that is shaping next-generation intelligent systems. You will have the opportunity to architect cutting-edge GenAI and RAG solutions for global clients in a collaborative and innovation-driven culture with deep technical mentorship. Role Overview: As an experienced AI/ML Architect, you will be responsible for leading the design and implementation of advanced Generative AI and RAG solutions at the enterprise level. This role will involve a combination of hands-on architecture design, pre-sales engagement, and technical leadership to drive AI initiatives that deliver business value responsibly and at scale. You will play a crucial role in designing and delivering enterprise-grade AI/ML solutions, engaging in pre-sales activities, and shaping AI solution proposals for clients. 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 by collaborating with business development, presenting technical solutions, estimating effort, and supporting proposals/PoCs for prospects. - Design knowledge retrieval layers using vector databases such as FAISS, Pinecone, Milvus, Chroma, and Weaviate. - Develop document ingestion, embedding, and context-retrieval pipelines for both unstructured and structured data. - Architect and manage MCP servers for secure context exchange, multi-model orchestration, and agent-to-agent collaboration. - Define LLMOps / MLOps best practices including 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 and TIC team to provide mentorship and guidance to AI developers. Qualification Required: - 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. - Prov
