Careers
AI Engineer — LLM & Agent Systems
Overview
Twinlink is seeking an AI Engineer to build production AI systems powered by large language models, retrieval, intelligent agents, and automation. You will work on AI capabilities that go beyond simple model API integrations. The role involves designing agent workflows, retrieval systems, tool integrations, evaluation pipelines, context and memory systems, and reliable backend infrastructure for AI-powered products. You will collaborate with product engineers, backend engineers, designers, and product teams to transform rapidly evolving AI technologies into useful, reliable, and scalable products. We are looking for an engineer who enjoys experimentation but also understands the engineering discipline required to move AI systems from prototypes into production.
Responsibilities
• Design and build production applications using large language models and generative AI. • Develop AI agents capable of reasoning across workflows, using tools, accessing external systems, and completing multi-step tasks. • Design and implement retrieval-augmented generation (RAG) systems. • Build document ingestion, chunking, embedding, indexing, retrieval, and reranking pipelines. • Integrate commercial and open-source AI models into Twinlink products. • Develop structured tool-calling and function-calling systems for AI agents. • Build context management, conversation state, memory, and orchestration systems. • Design evaluation frameworks for measuring AI quality, reliability, accuracy, latency, and cost. • Develop safeguards, validation layers, permission controls, and fallback mechanisms for production AI systems. • Optimize prompts, retrieval strategies, model selection, context usage, and inference costs. • Build APIs and backend services supporting AI-powered product features. • Integrate AI systems with databases, business applications, external APIs, and automation workflows. • Experiment with emerging models, frameworks, and AI architectures and determine their practical value. • Monitor production AI behavior and investigate failures or unexpected model outputs. • Collaborate with product teams to turn customer problems into practical AI capabilities. • Contribute to Twinlink's long-term AI engineering architecture and technical standards.
Requirements
• 3+ years of professional software engineering, machine learning engineering, AI engineering, or closely related experience. • Strong proficiency with Python. • Practical experience developing applications using large language models. • Experience working with LLM APIs and modern generative AI development patterns. • Understanding of prompting, embeddings, vector search, retrieval, and RAG architectures. • Strong backend engineering fundamentals. • Experience designing and consuming APIs. • Experience working with SQL databases. • Strong understanding of data structures, algorithms, and software architecture. • Ability to evaluate AI output critically rather than assuming model responses are correct. • Strong debugging and experimentation skills. • Ability to move rapidly from prototype to maintainable production implementation. • Experience using Git and collaborative software development workflows. • Strong English communication skills. • Legally authorized to work in India.
Preferred qualifications
• Experience building production AI agents or multi-step agentic workflows. • Experience with tool calling, structured outputs, agent orchestration, or model routing. • Experience with advanced RAG architectures, hybrid search, reranking, or semantic retrieval. • Experience with vector databases such as pgvector, Pinecone, Weaviate, Milvus, or similar technologies. • Experience with PostgreSQL, Redis, queues, and asynchronous processing. • Experience implementing AI evaluation and observability systems. • Familiarity with hallucination mitigation, guardrails, prompt injection risks, and AI security. • Experience with open-source models and model serving. • Familiarity with fine-tuning, distillation, or model adaptation techniques. • Experience with Docker, Kubernetes, and cloud infrastructure. • Experience building AI-powered SaaS or enterprise products. • Experience integrating AI agents with business applications, browsers, developer tools, or external services.