Artificial Intelligence Engineer

To Apply: https://forms.gle/G7pJQrs4wY3YrSip9

Role Summary

Design, build, and optimize production-grade Generative AI and Agentic AI systems for intelligent automation. Work across LLMs, agent orchestration, RAG, model adaptation, and scalable AI services.

Key Responsibilities

• Build agentic and multi-agent systems using Python, LLM APIs, LangGraph/LangChain, tool calling, and structured workflows.
• Develop RAG pipelines using embeddings, vector databases, semantic retrieval, reranking, and context engineering.
• Fine-tune and adapt LLMs/SLMs using SFT, LoRA/QLoRA, PEFT, and quantization where appropriate.
• Build scalable AI services and inference pipelines using FastAPI, asynchronous programming, and serving technologies such as vLLM.
• Integrate AI systems with enterprise APIs, databases, and business tools using tool calling and protocols such as MCP.
• Build evaluations and observability covering quality, hallucination, task completion, latency, token usage, cost, and execution traces.
• Optimize accuracy, latency, throughput, memory usage, and inference cost.
• Design reliable workflows with state, memory, guardrails, validation, retries, error handling, and human oversight.
• Apply security, privacy, access-control, and responsible-AI practices.
• Turn promising experiments into production-ready systems; contribute to architecture, deployment, and engineering best practices.
• Collaborate with product, engineering, data, and domain teams to translate operational problems into AI solutions.
• Support customer engagements as needed: understand workflows, explain technical choices, and assist with integration and deployment.