Senior AI Engineer
New
J
JobgetherAI Engineering
IndiaFull-TimeSenior
SalaryCompetitive compensation aligned with experience, skills, and market standards.
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Job Details
- Required Skills
- AWSDockerNode.jsPythonJavascriptTypeScriptAngularLLM
Requirements
- Demonstrated hands-on experience building and successfully shipping LLM-powered features or applications into production.
- Practical experience with technologies and patterns such as RAG, embeddings, vector search, agentic systems, LLM APIs, orchestration, and tool calling.
- Strong understanding of production LLM considerations, including evaluation, observability, failure modes, latency, reliability, and cost management.
- Experience improving AI features based on telemetry, evaluation results, production behavior, or user feedback.
- Strong software engineering background with experience designing APIs, backend services, and production-grade distributed systems.
- Full-stack development experience with technologies such as Angular, JavaScript/TypeScript, Python, Node.js, or comparable technologies.
- Ability to write clean, maintainable, well-structured code and take ownership of solutions from implementation through production.
- Experience working with AWS or comparable cloud infrastructure, CI/CD pipelines, and production environments.
- Strong product mindset with a bias toward shipping, rapid iteration, and measurable outcomes.
- Strong communication and collaboration skills, with the ability to work effectively with Product, ML, and Engineering teams.
Responsibilities
- Architect, develop, deploy, and maintain production-grade LLM capabilities integrated into customer-facing products and internal workflows.
- Build backend services, APIs, integrations, and supporting infrastructure that connect AI capabilities with product functionality.
- Contribute to full-stack and user-facing development where required, helping deliver complete AI-powered product experiences.
- Implement production-ready patterns involving LLMs, RAG, embeddings, vector search, agents, and tool calling.
- Design reusable components, abstractions, and engineering patterns that improve development velocity, consistency, and maintainability.
- Establish and improve evaluation frameworks, observability, monitoring, and feedback loops for AI-powered features.
- Monitor and optimize AI systems for quality, latency, reliability, scalability, and inference cost.
- Investigate production issues, analyze telemetry, and continuously improve AI functionality based on system performance and user feedback.
- Collaborate closely with Product, ML, and Engineering stakeholders to translate product challenges into practical AI solutions.
- Rapidly prototype and validate new ideas, then transform successful experiments into robust and maintainable production systems.
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