AI Engineer

• Competitive salary
• Dental insurance
• Health insurance
• Paid time off
• Vision insurance

We are looking for a passionate and hands-on AI Engineer to design and develop AI-powered applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI, and modern machine learning frameworks. The ideal candidate has strong Python fundamentals, practical project experience, and the ability to turn emerging AI capabilities into reliable product prototypes and production-ready services.

• Develop AI-powered applications and services using Python and modern AI/ML frameworks.
• Build and integrate LLM-based solutions using OpenAI, Llama, Hugging Face, or similar foundation models and platforms.
• Develop RAG applications using embeddings, vector databases, document chunking, retrieval, reranking, and grounding techniques.
• Build agentic AI workflows using Lang Chain, Lang Graph, tool/function calling, memory, and orchestration patterns.
• Develop REST APIs and lightweight backend services using FastAPI, Flask, or similar frameworks.
• Support prompt engineering, model/application evaluation, hallucination analysis, debugging, and performance optimization.
• Optimize token usage, prompt efficiency, context-window management, caching, latency, and LLM API costs while maintaining application quality.
• Prepare and transform structured and unstructured data for AI experimentation and product use cases.
• Use Git, Docker, automated testing, and CI/CD practices to support repeatable AI engineering workflows.
  
  Collaborate with product, cloud, data, QA, and solution architecture teams throughout the development lifecycle.
• Maintain technical documentation and evaluate new AI models, frameworks, and approaches for enterprise use cases.

• 1-4 years of professional, internship, research, or advanced project experience in AI/ML, Generative AI, or software engineering.
• Strong programming skills in Python.
• Solid understanding of Artificial Intelligence, Machine Learning, and Deep Learning fundamentals.
• Good understanding of Generative AI, LLM architectures, prompt engineering, embeddings, and vectorization.
• Hands-on project experience with OpenAI, Llama, Hugging Face, or similar models and platforms.
• Experience or strong exposure to Lang Chain, Lang Graph, RAG, vector databases, and Agentic AI concepts.
• Experience with token usage optimization, prompt efficiency, context-window management, caching, latency, and LLM API cost optimization.
• Familiarity with System One decision models.
• Basic experience building APIs with FastAPI, Flask, or similar Python frameworks.
• Working knowledge of Git, Docker, SQL, pandas/Num Py, and software engineering practices.
• Strong analytical, debugging, logical, and communication skills.

Candidates should demonstrate at least 2-3 hands‑on AI/GenAI projects involving one or more of the following:

• LLM-based applications or AI assistants.
• RAG solutions and vector database implementations.
• Agentic AI workflows or tool‑using agents.
• Prompt engineering and structured AI evaluation.
• Machine learning models using scikit‑learn, PyTorch, or Tensor Flow.
• FastAPI/Flask-based AI services.
• AI solutions solving a real‑world business or operational problem.

• Exposure to Azure AI, Azure Machine Learning, AWS Bedrock, Sage Maker, or equivalent cloud AI services.
• Experience with Pinecone, ChromaDB, FAISS, Qdrant, Milvus, pgvector, or similar vector databases.
• Exposure to Computer Vision, NLP, intelligent document processing, conversational analytics, or enterprise analytics.
• Experience applying token optimization techniques to improve the efficiency and cost of LLM-based applications.
• Experience with System One design.
• Experience…