Staff Software Engineer, Applied AI
New
S
SayariCommercial Intelligence
Remote, USFull-TimeStaff
Salary200,000 - 225,000 USD per year
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Job Details
- Experience
- 10+ years
- Required Skills
- AWSSQLElasticSearchGCPKubernetesTypeScriptAzureCassandraPostgresNosqlSparkRESTful APIsPrompt Engineering
Requirements
- 10+ years of industry experience in software development, including experience as an IC and a mentor/lead
- Hands-on experience building production applications with LLMs (Prompt engineering, RAG, fine-tuning, or agentic workflows)
- Deep understanding and proficiency with SQL databases
- Deep understanding and proficiency with NoSQL databases, including Postgres, Cassandra, Elasticsearch, and/or Cypher-based graph databases
- Proficiency in TypeScript
- Proficiency in backend web development (REST APIs)
- Experience managing software in cloud environments (GCP, AWS, or Azure)
- Experience conducting code reviews
- Experience conducting unit testing
- Experience with Graph databases and query optimization for large-scale data (desired)
- Experience working on both front end and back end code bases (desired)
- Experience with container orchestration via Kubernetes (desired)
- Interest in data visualization, or how to visually present large, complex datasets (desired)
- Exposure to batch processing frameworks like Spark (desired)
Responsibilities
- Lead implementation of one of the application team squads
- Oversee team's deliverables
- Architect solutions
- Ensure code quality and maintainability
- Work closely with product team and engineering leadership to ensure alignment with the product's roadmap
- Architect classical and AI powered solutions that are robust, scalable, and thoroughly tested
- Contribute directly to expanding the functionality and performance of our code base
- Collaborate with our Product and Design teams to evolve our platform roadmap
- Lead technical architecture decisions for AI features, balancing research ambitions with production reliability
- Establish evaluation frameworks and best practices for working with large language models in high-stakes, accuracy-critical domains
- Act as a multiplier for the engineering team through hands-on mentoring, deep code reviews, and technical training on AI skillsets
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