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Artificial Intelligence Engineer
4 weeks ago
Title - AI Engineer
Type - Contract
Location - Remote
Roles and Responsibilities:
- Design and implement AI solutions using LLMs for tasks such as summarization, question answering, and document understanding.
- Build and optimize retrieval-augmented generation (RAG) pipelines by combining OCR and LLMs for unstructured document processing.
- Integrate LLMs and OCR tools, such as Azure Document Intelligence and Tesseract, into scalable applications.
- Preprocess scanned and digital documents using OCR to extract structured and semi-structured text for LLM input.
- Perform prompt engineering and fine-tuning of LLMs to enhance accuracy and task-specific performance.
- Evaluate and monitor LLM+OCR system outputs for quality, bias, and hallucination risks.
- Collaborate with cross-functional teams to deliver AI solutions tailored to domain-specific needs.
- Stay updated on the latest developments in foundation models, Agentic AI, and OCR technologies.
Qualifications:
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
- 4+ years of experience in AI/ML, with a focus on LLMs and prompt engineering.
- Proven ability to work independently, demonstrating initiative, ownership, and accountability across all phases of solution development.
- Practical understanding of SAFe Agile methodologies, with experience delivering AI/ML solutions in cross-functional Agile teams.
Tools and Technologies:
- Hands-on experience with LLM frameworks such as LangChain and Semantic Kernel.
- Strong understanding of vector search, embedding models, and semantic retrieval (e.g., FAISS, Pinecone, Azure Cognitive Search).
- Proficiency in Python, with experience integrating APIs, function calling, regular expressions, and handling LLM outputs.
- Deep expertise in prompt engineering, including few-shot prompting, prompt tuning, prompt chaining, and optimization techniques for task-specific accuracy and cost control.
- Solid knowledge of RAG architectures and agent design patterns (e.g., tool use, memory, planning).
- Experience with OCR technologies (e.g., Tesseract, Azure Document Intelligence) for document understanding and information extraction from scanned or image-based content.
- Familiarity with LLM platforms and tools like Azure OpenAI, LLMOps.
- Experience deploying LLM-based applications in cloud environments, with attention to performance, reliability, and scalability.
