AI Engineer

Disprz is looking for an AI Engineer to design, build, and deploy scalable AI systems that power intelligent features across our product ecosystem. You will work closely with cross-functional teams to transform real business problems into production-ready AI solutions ranging from model development and optimization to integration with our platform.In this role, you should be deeply familiar with large language models and the fast-evolving AI ecosystem. You will be expected to experiment with, fine-tune, and optimize LLMs; design prompt and agent architectures; and integrate these models into reliable, scalable product experiences. Strong problem-solving, an ability to translate ambiguous requirements into practical AI workflows, and a passion for pushing the boundaries of applied generative AI are essential.Key Responsibilities:Design, develop, and optimize LLM-powered features, including prompt architectures, agent workflows, retrieval systems, and evaluation frameworks.Experiment with, fine-tune, and adapt state-of-the-art foundation models for domain-specific use cases.Build robust inference pipelines and integration layers that allow LLMs to operate reliably in production environments.Implement guardrails, monitoring, and quality-control mechanisms to ensure safety, accuracy, and consistency of AI outputs.Conduct rapid prototyping to validate AI-driven ideas and convert successful experiments into scalable product capabilities.Stay current with advances in generative AI, model APIs, tooling, and best practices—and continually identify opportunities to apply them within the platform.Translate ambiguous product requirements into clear technical solutions, collaborating closely with engineering, product, and design teams.Clearly articulate trade-offs, limitations, and expected performance of AI systems to both technical and non-technical stakeholders.Contribute to long-term AI strategy by proposing innovative solutions, architectures, and improvements across the product ecosystem.

Minimum 2 years of experience in an AI/ML engineering, NLP, or LLM-focused role. Experience working with LLMs and adapting them to real product use cases. Strong grasp of modern generative AI concepts, including prompt engineering, fine-tuning, and model evaluation. Strong programming skills in Python, with experience delivering production-ready code. Understanding of retrieval-augmented generation (RAG), vector databases, embeddings, and related techniques. Familiarity with modern infrastructure components used for deploying AI systems (e.g., REST APIs, Docker, cloud services). Familiarity with AI/ML frameworks and tooling such as PyTorch, Transformers, LangChain/LlamaIndex, or equivalent LLM orchestration stacks. B.E/B.Tech in Computer Science, Engineering or relevant field; OR graduate degree in Data Science or other quantitative field is preferred