Job Overview
We are looking for a talented
AI Engineer with over three years of experience in Large Language Models (LLMs), Conversational AI, and Generative AI. The ideal candidate will have hands-on experience building intelligent systems with LangChain and LangGraph, with a focus on AI agents, retrieval-augmented generation (RAG), and autonomous reasoning pipelines.
You'll collaborate with cross-functional teams to design, implement, and optimize AI-driven solutions that enhance business processes and user experiences.
Roles & Responsibilities
- Design, fine-tune, and deploy LLMs for production-ready applications.
- Build AI agents and multi-agent systems using LangChain, LangGraph, and related frameworks.
- Develop and integrate Conversational AI pipelines that deliver seamless, human-like interactions.
- Implement Retrieval-Augmented Generation (RAG) architectures leveraging vector databases and document retrievers.
- Collaborate with data engineers and product teams to design scalable, reliable, and efficient AI pipelines.
- Develop and maintain APIs and backend services using FastAPI and modern software engineering practices.
- Collaborate with data engineers and product teams to design scalable, reliable, and efficient AI pipelines.
- Optimize model inference, latency, and scalability in cloud and containerized environments
- Optimize model inference, latency, and scalability in cloud and containerized environments.
- Stay current with advancements in LLMs, Agentic AI, and Generative AI ecosystems.
Required Skills
- Strong proficiency in Python and experience with LangChain and LangGraph for AI agent orchestration.
- Experience integrating LLMs via APIs such as OpenAI, Anthropic, or Google Vertex AI.
- Solid understanding of prompt engineering, RAG pipelines, and tool-using AI agents.
- Proficiency with PyTorch, Transformers (Hugging Face), and vector databases (e.g., Qdarnt, FAISS, Chroma).
- Experience designing multi-agent systems or workflow-based AI agents using LangGraph or similar frameworks.
- Knowledge of LLM evaluation, prompt optimization, and context management strategies.
- Familiarity with cloud platforms (AWS, Azure, GCP) and containerization tools (Docker, Kubernetes).
Preferred Qualifications
- Familiarity with MLOps and continuous integration/deployment pipelines for AI systems.
- Understanding of generative AI models for text, image, or multimodal outputs.
- Familiar with traditional ML and computer vision.
- Strong problem-solving, collaboration, and communication skills.
Qualifications
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
- 3+ years of hands-on experience in AI/ML model development and deployment.
