Build and scale AI/ML and GenAI pipelines from experimental workflows to production-ready systems.
Integrate model training, evaluation, deployment, and monitoring into product workflows.
Deploy and manage GenAI solutions such as chatbots, RAG applications, and predictive analytics tools.
Operationalize LLMs and AI agents, including prompt orchestration, chaining, and fine-tuning.
Benchmark models, develop evaluation frameworks, and improve reliability and auditability.
Implement observability, monitoring, and rollback mechanisms to ensure secure, scalable deployments.
Work across the stack—from backend systems to product SDKs—to deliver AI features directly into user-facing applications.
Prototype rapidly, gather feedback, and iterate while keeping scale and maintainability in mind.
Own critical product components and take responsibility for delivering robust, production-grade features.
Collaborate cross-functionally with data scientists, product managers, and engineers to scope specifications and solve real customer problems.
Debug complex issues and perform root cause analysis across model pipelines, infrastructure, and product layers to ensure reliability and continuous improvement.

BS or MS in Computer Science, Statistics, or Mathematics, or equivalent experience.
Strong software engineering background with proven experience shipping production systems.
3+ years of experience in ML/DL pipelines, deployment, and applied AI solutions.
Proficiency in Python or Go with frameworks like TensorFlow, PyTorch, Scikit-Learn, FastAPI, or gRPC.
Experience with LLM and AI frameworks such as Langchain, LlamaIndex, Hugging Face Transformers, and OpenAI API.
Knowledge of RAG architectures, embeddings, reranking models, and LLM-based dialogue systems.
Experience building and scaling backend platforms, APIs, and microservices.
Comfortable working full-stack, from model APIs down to user-facing integrations.
Have shipped AI features that users actually use; production experience over theoretical knowledge.
Track record of building reliable products with strong attention to detail and usability.
Autonomous and excited about taking ownership over major initiatives.
Frequent user of AI products (Cursor, Claude Code, Copilot, etc.) during the development lifecycle.

Software Engineer – AI Platform

Key skills