Software Engineer II, AI

Responsibilities

• Design and implement retrieval-augmented generation (RAG) systems with agentic workflows to refine query understanding, document retrieval, and response synthesis.
• Build and optimize retrieval pipelines using BM25, dense retrieval, hybrid retrieval, and re-ranking approaches.
• Develop evaluation pipelines for retrieval and generation, including offline metrics (recall, MRR, nDCG) and human-in-the-loop evaluations.
• Experiment with query rewriting, expansion, and classification to improve retrieval relevance.
• Collaborate closely with Product to bring ML-powered search agents into production.
• Profile, debug, and optimize the latency, accuracy, and scalability of retrieval and generation components.
• Contribute to the design of data pipelines for training retrieval and ranking models, including dataset curation, augmentation, and labeling workflows.
• Stay up-to-date with advancements in LLMs, retrieval techniques, and agent architectures, evaluating opportunities to integrate them into our systems.
• What You Bring
• Software engineering experience
• Experience with information retrieval systems, search relevance, and ranking models
• Expertise in Python, with experience in frameworks such as PyTorch, TensorFlow, or JAX.
• Familiarity with LLMs, prompt engineering, and retrieval-augmented generation pipelines.
• Understanding of evaluation methods for search systems, including offline metrics and user-facing evaluation.
• Experience working with vector database infrastructure (FAISS, Milvus, Weaviate, Pinecone, PGVector) and traditional search engines (Elasticsearch, OpenSearch)
• Understanding of data pipelines, preprocessing, and large-scale data handling.
• Ability to work independently and collaboratively in a fast-paced environment, balancing research and production needs.
• Develop and implement CI/CD pipelines. Automate the deployment and monitoring of ML models.
• Knowledge of query understanding, document summarization and other content enrichment strategies
• Expertise in automated LLM evaluation, including LLM-as-judge methodologies
• Skilled at prompt engineering - including zero-shot, few-shot, and chain-of-thought.
• Experience with cloud infrastructure (AWS, GCP, Azure) for scalable ML workflows.

Requirements

• Experience with agentic system design for LLM workflows.
• Background in conversational search.
• Contributions to open-source projects in the retrieval, NLP, or LLM ecosystems.
• What Success Looks Like
• We're looking for builders who:
• Take ownership and run with ambiguous problems
• Jump into new areas and rapidly learn what's needed to deliver solutions
• Bring scientific rigor while maintaining a pragmatic delivery focus
• See unclear requirements as an opportunity to shape the solution
• Our Tech Stack
• LLM platforms: OpenAI, Anthropic, open-source models
• ML frameworks: PyTorch, Transformers, spaCy
• Search/Vector DBs: Elasticsearch, Pinecone, PostgreSQL
• MLOps tools: Weights & Biases, MLflow, Langfuse
• Infrastructure: Docker, Kubernetes, GCP
• Development: Python, Git, CI/CD
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• At Klue, we're committed to building a high-performing team where people feel a strong sense of belonging, can be their authentic selves, and are able to reach their full potential. If there's anything we can do to make our hiring process more accessible or

Additional Information

At Klue , We're Building the Future of Competitive Intelligence 👋 Klue Engineering is hiring! We're looking for a Software Engineer, AI to join our team in Toronto, focusing on building and optimizing state-of-the-art LLM-powered agents that can reason, plan and automate workflows for users. You'll be joining us at an exciting time as we reinvent our insight generation systems, making this an excellent opportunity for someone with strong Backend and ML fundamentals who wants to dive deep into practical LLM applications. As a member of our team, you'll be leading the design and implementation of search and retrieval agent systems that enable users to discover high-quality, relevant information with minimal effort. You will work at the intersection of LLM-powered agent workflows, retrieval pipelines, and evaluation frameworks, ensuring that our systems remain scalable, efficient, and aligned with user intent.

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