We are seeking a highly skilled Artificial Intelligence Engineer with hands-on experience in Python, Retrieval-Augmented Generation (RAG), LangChain, LangGraph, and modern Agentic AI frameworks. The ideal candidate will architect, build, and optimize intelligent systems and workflows that leverage LLMs to solve complex business problems.
You will collaborate with cross-functional teams to develop scalable AI applications, autonomous agents, and knowledge systems that integrate with real-world products.
Key Responsibilities
- Design, build, and maintain LLM-powered applications, including chatbots, autonomous agents, and decision-support tools.
- Develop and optimize RAG pipelines using vector databases, embeddings, metadata filtering, and document preprocessing.
- Implement workflow orchestration using LangGraph, including event-driven agents, state management, and multi-step AI reasoning.
- Build AI solutions using LangChain components such as chains, tools, agents, memory, and custom integrations.
- Develop Agentic AI systems capable of multi-step task execution, planning, and reasoning.
- Write clean, scalable Python code for model serving, data processing, and API integrations.
- Fine-tune models, evaluate LLM outputs, and ensure reliability, grounding, and safety.
- Integrate AI systems with existing backend services, cloud platforms, and product architectures.
- Collaborate with ML engineers, data engineers, and product teams to deliver production-quality solutions.
- Monitor, evaluate, and optimize model performance, latency, and retrieval quality.
Required Skills & Qualifications
- Strong proficiency in Python and modern Python frameworks.
- Practical experience building RAG systems using tools like:
- FAISS, Chroma, Pinecone, Weaviate, or similar vector DBs.
- Hands-on experience with LangChain (chains, agents, tools, retrieval, memory).
- Strong understanding of LangGraph workflows and agent orchestration.
- Deep understanding of LLMs, embeddings, tokenization, prompt engineering, and evaluation.
- Experience building Agentic AI architectures (planning, reasoning, multi-agent workflows).
- Familiarity with LLM APIs (OpenAI, Anthropic, Groq, HuggingFace, etc.).
- Knowledge of REST APIs, microservices, and backend integration.
- Experience with cloud platforms (AWS, GCP, Azure) and containerization (Docker).
- Strong debugging, problem-solving, and algorithmic thinking skills.
