AI Developer

We are looking for an AI Developer with 2-4 years of software/AI development experience to build and evolve customer-facing AI capabilities. This is a hands-on engineering role focused on building production AI systems that understand user conversations, maintain relevant context, retrieve information, interact with application services and tools, and generate useful, safe, and reliable responses. We are looking for someone who understands that production AI engineering goes beyond prompt writing. You should be comfortable working across LLMs, Python, APIs, RAG, memory, structured outputs, evaluation, observability, and production debugging.

Responsibilities:

• Develop conversational and generative AI applications using modern LLMs.
• Build and maintain AI backend services using Python and FastAPI or similar frameworks.
• Implement prompt and context engineering, structured outputs, streaming, and tool/function calling.
• Build RAG pipelines using embeddings, vector search, metadata filtering, reranking, and contextual retrieval.
• Design short-term and long-term conversational memory and personalization mechanisms.
• Integrate AI capabilities with internal APIs, databases, and business systems.
• Develop AI workflows and agentic capabilities where appropriate.
• Implement model routing, retry, timeout, fallback, and error-handling strategies.
• Build safeguards for prompt injection, inappropriate tool usage, PII handling, and unreliable model responses.
• Develop automated evaluations and regression tests for AI behavior.
• Monitor AI systems for response quality, latency, errors, token consumption, and cost.
• Debug and continuously improve AI behavior based on production observations.

Requirements:

• 2-4 years of software/AI engineering experience.
• Strong Python programming skills.
• Experience with FastAPI or similar backend frameworks.
• Hands-on experience integrating LLM APIs.
• Good understanding of prompt and context engineering.
• Experience with structured LLM outputs and/or function calling.
• Understanding of RAG architecture.
• Understanding of embeddings and vector search.
• Experience integrating REST APIs and third-party services.
• Understanding of asynchronous programming.
• Experience with Git and standard software development practices.
• Ability to write maintainable and testable production code.
• Strong debugging and problem-solving skills.
• Candidates should have a practical understanding of LLMs and transformer fundamentals, tokens and context windows, embeddings and semantic similarity, RAG, prompt and context engineering, tool/function calling, structured generation, hallucination and grounding, prompt injection, model latency, quality and cost trade-offs, and evaluation of non-deterministic AI systems.