AI Engineer

Job Summary
We are seeking a talented and innovative AI Engineer to join our growing engineering team. In this role, you will bridge the gap between AI research and practical software products. You will be responsible for implementing, optimizing, and deploying AI models—specifically Large Language Models (LLMs) and predictive algorithms—into our core product infrastructure to automate processes and deliver intelligent user experiences.

Key Responsibilities
Model Integration & Software Development
Build and deploy production-ready AI applications and workflows using APIs and microservices.Utilize pre-trained foundation models (e.g., OpenAI GPT, Anthropic Claude, Google Gemini, Meta Llama) to develop features like chatbots and automation tools.Design RAG pipelines and semantic search systems to query proprietary datasets securely.Write clean, scalable code in Python, Java, or C++ to embed AI functionality into existing software infrastructure.

Data Engineering & Infrastructure
Develop data pipelines for data ingestion, transformation, and feature engineering.
Manage vector databases (e.g., Pinecone, Milvus, Chroma) and optimize embedding models.
Deploy AI systems to cloud platforms using tools like AWS SageMaker or Google Cloud Vertex AI.

Optimization & Collaboration
Apply prompt engineering techniques and in-context learning to maximize model efficiency.Fine-tune models and monitor performance, latency, and costs in production.Partner with cross-functional teams, including Data Scientists, Product Managers, and frontend engineers, to align AI solutions with business goals.

Required Skills & Qualifications
Technical CompetenciesProgramming Languages: Mastery of Python is required; experience with Java, C++, or R is a plus.AI Orchestration Frameworks: Deep experience with LangChain, LangGraph, or LlamaIndex.Machine Learning Libraries: Familiarity with PyTorch, TensorFlow, Keras, or Hugging Face Transformers.Core Concepts: Strong understanding of transformer architectures, attention mechanisms, and tokenization.Mathematics: Practical knowledge of linear algebra, calculus, probability, and statistics.

Soft Skills & Professional
AttributesProblem-solving: Ability to creatively troubleshoot unpredictable model behaviors or "hallucinations".
Communication: Capacity to explain complex technical AI behaviors to non-technical stakeholders and executives.
Adaptability: A proactive mindset geared toward keeping up with weekly advancements in the open-source and frontier AI spaces.

Education & Experience Requirements
Degree: Bachelor's degree in Computer Science, Data Science, AI, Mathematics, or a highly technical related field (Master's or Ph.D. preferred for research-heavy roles).
Experience: 2–5+ years of software engineering experience, with at least 1–2 years explicitly focused on building and shipping AI-powered features or machine learning workflows.