GenAI Engineer (LLM Copilots, RAG, Agentic Workflows)

About the role

The GenAI Engineer designs, builds and deploys enterprise-grade Generative AI applications that accelerate digital transformation. The role focuses on hands-on engineering of LLM-powered systems such as copilots, RAG solutions, agentic workflows and AI-driven automations, working with Sales, HR, Operations, Finance and IT to turn business problems into scalable, secure, production-ready solutions. It also helps build internal AI capability through reusable components, reference architecture and documentation.

Key responsibilities

• Design, build and deploy end-to-end GenAI applications: LLM-based copilots, chatbots, RAG pipelines, multi-agent systems and multimodal workflows.
• Develop and optimize RAG solutions using vector databases, embeddings, metadata filtering and grounding techniques.
• Implement prompt engineering, tool/function calling and agent orchestration for reliable task execution and automation.
• Integrate GenAI solutions with enterprise systems and data sources via APIs, event-driven architectures and secure connectors.
• Collaborate with product owners and business stakeholders to turn requirements into technical architectures and implementation plans.
• Establish LLMOps best practices: deployment pipelines, monitoring, logging, evaluation, cost optimization and performance tuning.
• Implement AI safety, governance and guardrails, including content filtering, grounding validation, access control and compliance.
• Evaluate emerging GenAI models, frameworks and tooling to improve quality, latency and cost efficiency.
• Contribute to internal capability building with reusable libraries, templates, documentation and reference implementations.

Required profile

• 3-5 years of experience in software engineering, AI engineering, ML engineering or related fields.
• At least 1 year of hands-on Generative AI development, building and deploying LLM-based applications.
• Bachelor's degree in computer science, computer engineering, information technology, artificial intelligence or a related discipline.
• Preferred: Master's degree or PhD, 4+ years of experience, proven production-grade GenAI delivery, and experience with LLMOps, MLOps, CI/CD and cloud-native architectures.

Required skills

• Strong Python and API/backend service development.
• Practical experience with LLMs, embeddings, vector databases and RAG architecture.
• Familiarity with GenAI frameworks such as LangChain, Semantic Kernel or LlamaIndex.
• Experience with cloud AI platforms (Azure, Databricks, NVIDIA or equivalent).
• Preferred certifications: Databricks Certified Generative Engineering Associate, Microsoft Generative AI Engineering Professional Certificate, Microsoft Certified: Azure AI Engineer Associate, NVIDIA Generative AI / LLMs Certified Associate, IBM Generative AI Engineering Professional Certificate.

Working arrangement: hybrid.