AI Solution Architect (LLM & Generative AI)

Greetings from Veltris!

Please find the details below and interested can share your updated resumes at chiranjeevi.dhoradla@veltris.com at the earliest.

• Role: AI Solution Architect (LLM & Generative AI)
• Experience: 13+
• Location: Hyderabad / Remote
• Shift Timings: UK Timings (12/1 PM to 9/10 PM IST) or General Timings
• Notice Period: 0-15 Days/30 Max (Due to project dependency)
• No. of open positions: 1

Job Summary:We are looking for an accomplished AI Solution Architect to define and lead the architecture of enterprise-grade AI solutions powered by large language models and agentic systems. In this role, you will partner with clients, product teams, and engineering leaders to translate complex business problems into scalable, secure, and production-ready AI platforms.

Key Responsibilities:

• Define end-to-end architectures for LLM-powered applications, including retrieval-augmented generation (RAG), agentic workflows, and multi-model orchestration.
• Engage with clients and stakeholders to understand business objectives, assess AI feasibility, and design fit-for-purpose solutions.
• Select and evaluate foundation models, open-source and proprietary, and decide between prompt engineering, fine-tuning, and retrieval strategies based on cost, latency, accuracy, and risk.
• Design robust evaluation frameworks, guardrails, and monitoring to ensure quality, safety, and reliability of AI systems in production.
• Architect scalable, secure, and observable AI platforms on cloud environments such as AWS, Azure, or Google Cloud.
• Establish best practices for responsible AI, including data privacy, security, governance, bias mitigation, and compliance.
• Provide technical leadership and mentorship to engineers, data scientists, and ML engineers across projects.
• Create reference architectures, proofs of concept, and technical documentation, and present solutions to both technical and executive audiences.
• Track emerging research and technologies, and assess their applicability to client and internal use cases.

Preferred candidate profile:

• Published research or patents in machine learning, natural language processing, or generative AI.
• Experience with LLMOps and MLOps practices, including model serving, evaluation pipelines, observability, and versioning.
• Experience building multi-agent systems with planning, memory, and tool-use capabilities.
• Familiarity with model optimization techniques such as quantization, distillation, and efficient inference.
• Experience with distributed data and streaming technologies such as Kafka, Spark, or Flink.