Senior AI Engineer - Chat & Agent Systems
New
J
JobgetherAI software
Fully remote position based in India.Full-TimeSenior
Salary not disclosed
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Job Details
- Experience
- Approximately 4–5 years of professional software engineering experience.
- Required Skills
- PythonData modeling
Requirements
- Have approximately 4–5 years of professional software engineering experience.
- Demonstrate strong proficiency in Python, including idiomatic, typed, testable, and maintainable production code.
- Bring solid programming fundamentals and sound engineering judgment.
- Have experience designing, delivering, and operating production systems.
- Understand API design, data modeling, concurrency, error handling, and observability.
- Have experience writing automated tests using tools such as pytest.
- Have hands-on experience building LLM-powered or agentic applications.
- Have experience building tool-calling or multi-step workflows using LangGraph or comparable agent frameworks.
- Understand conversation state, context management, execution flows, model APIs, structured outputs, retrieval, and grounding techniques.
- Have experience with streaming responses, asynchronous execution, and evaluating prompts, models, and end-to-end agent behavior.
- Have experience instrumenting and debugging LLM applications with Langfuse, LangSmith, or similar observability platforms.
- Be highly proficient in agentic coding workflows, including critically evaluating generated code and designing workflows that maintain engineer accountability.
Responsibilities
- Own the technical design, architecture, and delivery of chat-team projects.
- Help define engineering priorities, standards, and day-to-day technical decisions.
- Translate product requirements into simple, scalable, and maintainable system designs.
- Write high-quality production Python code and develop clear abstractions.
- Review pull requests for correctness, maintainability, test coverage, and design quality.
- Mentor junior developers in programming fundamentals, system design, and code quality.
- Diagnose production issues across application code, agent workflows, prompts, models, data, and infrastructure.
- Improve system reliability, observability, latency, and operating costs.
- Promote the use of AI coding agents across exploration, planning, implementation, testing, debugging, and code review.
- Identify engineering workflows to automate and help the team adopt responsible agentic coding practices.
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