AI Engineer
Key Responsibilities
- Design, build, and deploy LLM-powered applications for real-world use cases (e.g., document intelligence, copilots, knowledge systems)
- Develop and optimize Retrieval-Augmented Generation (RAG) pipelines, including ingestion, chunking, embedding, retrieval, and reranking
- Architect and implement end-to-end AI systems, from backend services to user-facing interfaces
- Work with structured and unstructured data, ensuring data quality, governance, and efficient retrieval
- Implement evaluation frameworks to measure model performance, latency, and hallucination rates
- Collaborate with data engineers and ML engineers to productionize models and pipelines
- Deploy and maintain systems on cloud platforms (AWS, GCP, or Azure), ensuring scalability and reliability
- Stay up to date with the latest advancements in LLMs, agent frameworks, and AI tooling
Requirements
- Bachelor's or Master's degree in Computer Science, Engineering, or related field
- 3-8+ years of experience in software engineering or applied AI roles
- Hands-on experience building LLM or GenAI applications in production environments
- Strong understanding of RAG architectures and vector search
- Proficiency in Python and at least one backend framework (FastAPI, Flask, or similar)
- Experience with LLM frameworks (e.g., LangChain, LlamaIndex, OpenAI SDKs)
- Strong problem-solving skills and ability to work in a fast-paced, evolving environment
What's on Offer
- Opportunity to build greenfield AI systems with real business impact
- High level of ownership and autonomy in shaping AI architecture
- Collaborative environment with strong technical leadership
- Exposure to cutting-edge AI use cases across industries
FAQs
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