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Job Title: AI Engineer
Role Summary
Lead design, development, and deployment of AI solutions focused on risk modelling, fraud detection, claims automation, and personalized retirement planning within the insurance domain. Drive end‑to‑end implementation of retrieval‑augmented generation (RAG) pipelines, large language model (LLM) fine‑tuning, and vector‑based retrieval, ensuring scalable, performant, and cost‑effective systems that meet compliance and operational efficiency goals.
Expectations
• 6+ month contract, hybrid remote/office.
• Deliver production‑grade AI applications on schedule, collaborating with data scientists and software engineers.
• Continuously monitor, benchmark, and improve model performance and system latency.
• Communicate progress and research findings to cross‑functional stakeholders.
Key Responsibilities
• Design and implement RAG solutions combining retrieval systems with LLMs.
• Build and maintain AI workflows using LangChain, orchestrating chains, prompts, and agents.
• Deploy and manage vector databases (FAISS, Pinecone, Weaviate, Milvus) for similarity search.
• Develop embedding pipelines for unstructured data (text, documents, images) via Sentence Transformers, OpenAI, or Hugging Face models.
• Fine‑tune and evaluate GPT, BERT, and other large language models for domain‑specific tasks.
• Optimize retrieval and generation pipelines for speed, accuracy, and cost.
• Integrate AI models into production products, ensuring reliability and scalability.
• Conduct research to enhance existing AI systems and pioneer new approaches.
• Stay current with advances in generative AI, vector search, and related technologies.
• Collaborate with cross‑functional teams to align AI solutions with business objectives.
Required Skills
• Strong experience in AI / machine learning / deep learning.
• Proficiency in Python; familiarity with R or Java acceptable.
• Expertise with TensorFlow, PyTorch, or Keras.
• Experience fine‑tuning and prompt engineering for large language models (GPT, BERT, etc.).
• Hands‑on knowledge of vector similarity search and vector databases (FAISS, Pinecone, Weaviate, Milvus).
• Ability to create, test, and deploy embeddings using Sentence Transformers, OpenAI, or Hugging Face.
• Familiarity with LangChain for building LLM‑powered applications.
• Proven ability to deploy AI models in production environments.
• Understanding of NLP fundamentals; experience with computer vision or reinforcement learning is a plus.
Required Education & Certifications
• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field.
• Relevant certifications (e.g., TensorFlow Developer, PyTorch, Azure AI Engineer) are advantageous.