Senior Software Engineer (AI / LLMs)
New
A
ApiphaniManaged Services
RemoteFull-TimeSenior
Salary120,000 - 160,000 USD per year
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Job Details
- Experience
- 6+ years
- Required Skills
- AWSDockerPostgreSQLPythonTypeScriptTerraformGitHub Actions
Requirements
- 6+ years of backend engineering experience in production environments.
- Strong proficiency in Python and TypeScript for building distributed, event-driven systems.
- Deep understanding of AWS services (Lambda, ECS, Bedrock, S3, CloudWatch, etc.).
- Experience designing APIs, microservices, and event pipelines that interface with LLMs or AI models.
- Familiarity with vector databases and concepts like embeddings, similarity search, and retrieval-augmented generation.
- Experience with infrastructure-as-code tools such as Terraform or AWS CDK.
- Understanding of SQL and schema migration workflows (PostgreSQL or similar).
- Hands-on experience with Docker, GitHub Actions, and cloud-native CI/CD workflows.
- Ability to diagram systems, communicate architecture decisions clearly, and work asynchronously in a distributed team.
- Strong sense of ownership and ability to deliver in fast-moving, ambiguous environments.
Responsibilities
- Design and implement backend services that enable intelligent agent workflows and autonomous infrastructure actions.
- Develop APIs and orchestration layers in Python and TypeScript, integrating LLMs, vector databases, and observability pipelines.
- Build scalable systems to support LLM-based reasoning, retrieval, and decision-making across cloud infrastructure.
- Integrate with AWS Bedrock and other LLM platforms to support multi-model capabilities.
- Develop data access and semantic search layers using vector databases (e.g., pgvector, Pinecone, Qdrant).
- Build robust monitoring, testing, and CI/CD systems to ensure reliability and reproducibility of AI workflows.
- Collaborate closely with the product and DevOps teams to design architecture diagrams, plan deployments, and monitor system health.
- Write clean, maintainable code with clear documentation and strong adherence to security and performance best practices.
- Participate in code reviews, design discussions, and iterative delivery cycles to improve product velocity and quality.
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