Build scalable AI systems

• Design and operate machine learning services and workflows using Python, Docker, Kubernetes, and AWS.
• Build reliable pipelines for batch and real time processing using technologies such as Kafka and Airflow.
• Design solutions using vector databases to support retrieval, recommendations, personalisation, and semantic search.
• Use MLflow to manage experiments, model versions, registries, and deployments.
• Improve the reliability, performance, scalability, and cost efficiency of our AI systems.

Evaluate and improve AI quality

• Build automated evaluation pipelines for generative AI applications.
• Develop benchmarks that measure accuracy, relevance, reliability, fairness, latency, and cost.
• Evaluate retrieval strategies, including chunking, embeddings, context selection, and reranking.
• Monitor AI systems in production and identify opportunities to improve their quality and performance.
• Create safeguards that reduce unexpected behaviour and protect customer data.

Shape our AI engineering practice

• Establish reusable patterns and technical standards for building, evaluating, and releasing AI systems.
• Help teams make informed decisions about models, frameworks, infrastructure, performance, and cost.
• Apply strong engineering practices across testing, security, observability, version control, and deployment.
• Share technical knowledge and support the development of other engineers.
• Keep up with relevant AI research, tools, and engineering practices, applying what is useful to Typeform.

Collaborate across Typeform

• Partner with Product, Engineering, Data Science, Data Engineering, and Analytics teams to connect AI investments with customer and business needs.
• Work with Data Scientists to turn experiments and models into reliable production services.
• Communicate technical concepts, risks, and tradeoffs clearly to technical and nontechnical partners.
• Contribute to technical planning and help shape the direction of AI across Typeform.

What you bring

• At least four years of experience building and deploying machine learning or AI systems in production.
• Strong Python and software engineering skills.
• Experience building production services using Python frameworks such as FastAPI.
• Practical experience developing generative AI applications using large language models, RAG, tool use, or agentic systems.
• Experience with frameworks such as PyTorch, LangChain, LangGraph, or similar technologies.
• A strong understanding of enterprise RAG systems, including chunking, embeddings, retrieval, reranking, evaluation, and monitoring.
• Experience creating automated evaluations for generative AI applications.
• Experience with AWS, Docker, Kubernetes, Terraform, and continuous integration and deployment practices.
• Experience using services such as AWS SageMaker or AWS Bedrock.
• Experience with Kafka, vector databases, or other technologies used for real time and high dimensional data processing.
• Experience managing machine learning workflows using MLflow.
• Experience monitoring production systems with tools such as Datadog or OpenSearch.
• The ability to balance quality, speed, reliability, scalability, and cost when making technical decisions.
• Strong communication skills and experience collaborating with Product, Engineering, and Data teams.

Extra awesome

• Experience working in a B2B SaaS product company.
• Experience with orchestration tools such as Airflow or Argo Workflows.
• Familiarity with SQL, Spark, Snowflake, or other data processing technologies.
• Experience building systems that combine structured data, unstructured data, and generative AI.
• Experience with AI security, privacy, responsible AI, prompt injection protection, or data leakage prevention.
• Experience improving the latency and cost of AI systems operating at scale.

Originally posted on Himalayas