Software Engineer

• Own backend features end-to-end: discovery, design, implementation, rollout, and ongoing reliability and operations, with support from more experienced teammates as needed.

• Help design and evolve distributed systems (services, pipelines, and data stores) with an eye toward performance, scalability, and resiliency.

• Build and maintain APIs and data access patterns that support analytics and search use cases.

• Develop, maintain, and optimize scalable data pipelines that power product features, analytics, and machine learning workloads.

• Ensure data reliability, quality, and performance across our systems, and monitor and troubleshoot pipelines to ensure consistent, timely delivery.

• Build strong engineering habits: thoughtful code reviews, solid testing, incident readiness, and operational excellence.

• Apply an experimentation-first approach: define hypotheses and success metrics/guardrails, run controlled rollouts and A/B tests when appropriate, and write clear readouts for stakeholders.

• Use AI coding tools like Claude Code productively and responsibly as part of your development workflow - for implementation, debugging, refactoring, and design reviews - while maintaining high standards for correctness, security, and privacy.

• Bring evaluation discipline to AI-assisted work: treat prompts and configs like versioned artifacts, design regression tests, measure quality changes, and monitor for drift the same way you would for performance or correctness.

• Grow continuously: actively seek feedback, learn new tools, languages, and domains quickly, and apply what you learn to your work.

• Collaborate with Product Managers and fellow Engineers to ship intelligent, data-driven products.

• Share knowledge with teammates through clear documentation, pairing, and participation in code reviews.

• Document systems, pipelines, and architecture, and help evolve our engineering best practices.

• Stay current with emerging tools, frameworks, and trends across software, data, and AI engineering.

• A few years of professional software engineering experience building backend systems, and a desire to grow into larger distributed systems challenges.

• A growth mindset: curiosity, a habit of learning new tools and domains quickly, and openness to feedback.

• Strong general-purpose programming skills and software engineering fundamentals.

• Solid debugging skills and the ability to troubleshoot and performance-tune production services.

• Strong SQL and data modeling skills.

• Experience with version control (Git) and CI/CD workflows.

• Comfort using AI coding assistants like Claude Code as part of your workflow, and the discipline to validate outputs (tests, metrics, evaluation) rather than trusting them blindly.

• Strong problem-solving and communication skills, and the ability to collaborate across functions.

• Experience building and maintaining data pipelines (ETL/ELT).

• Exposure to event-driven architectures, cloud deployment on AWS, and containers (Docker/Kubernetes).

• Hands-on experience building or deploying AI/ML-powered features or data-driven products.

• Familiarity with machine learning workflows, including data preparation, training, and deployment.

• Familiarity with ML libraries/frameworks (e.g., scikit-learn, TensorFlow, PyTorch, or similar).

• Exposure to LLMs, NLP, or generative AI use cases.

• Experience with Databricks, Apache Spark, or similar distributed data platforms (including cost monitoring and optimization).

• Experience deploying ML models using MLOps tools (e.g., MLflow, Airflow, Kubeflow).

• Experience with workflow/orchestration tools (Airflow, Argo, Dagster), Terraform/Ansible, and Grafana dashboards.

• Search/retrieval systems (Elasticsearch/Lucene) and GraphQL.

• Understanding of real-time or streaming data pipelines.

Originally posted on Himalayas