AI Engineer
New
J
JobgetherAI software
AI Engineer based in India; Fully remote role based in India.Full-TimeMiddle
Salary not disclosed
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Job Details
- Experience
- 2–4 years of professional software engineering experience
- Required Skills
- Artificial IntelligenceSoftware EngineeringLLM
Requirements
- Have 2–4 years of professional software engineering experience; product-focused startup or scale-up experience is ideal.
- Bring strong software engineering fundamentals and an AI-native approach to building products.
- Have demonstrated experience building and shipping products or features powered by AI and large language models.
- Have strong hands-on experience with cloud infrastructure and cloud-native development.
- Have experience with LLM APIs, AI agents, retrieval-augmented generation (RAG), tool calling, MCP, or similar AI application patterns.
- Have strong programming skills in one or more modern programming languages.
- Be able to build production systems end to end and take ownership of technical outcomes.
- Provide a portfolio of tangible work, such as shipped AI projects, GitHub repositories, products, or demos.
- Have strong problem-solving, debugging, and systems-thinking skills.
- Be able to work as a hands-on individual contributor within a small engineering team.
- Be comfortable working in an ambiguous, fast-paced startup environment with evolving priorities.
Responsibilities
- Build and continuously improve the AI engineering harness and core AI capabilities powering the product.
- Develop production-grade AI features using foundation models and modern LLM technologies.
- Design and implement AI agents, tools, workflows, and integrations capable of taking meaningful actions.
- Build reliable, scalable infrastructure for AI workloads across cloud services.
- Experiment with emerging AI models, frameworks, developer tools, and engineering workflows.
- Turn prototypes and experiments into maintainable, production-ready systems.
- Improve AI reliability through evaluation frameworks, observability, logging, guardrails, testing, and monitoring.
- Integrate AI capabilities with APIs, databases, internal systems, and external tools.
- Own technical problems end to end, from architecture and implementation through deployment and iteration.
- Partner with founding leadership to identify and solve product and engineering challenges.
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