Lead AI Engineer

Job summary

Summary of Position: We are seeking a highly skilled and experienced Lead AI Engineer to join our dynamic team. The ideal candidate will excel at identifying and articulating complex business problems, and will develop innovative, scalable, and robust AI/ML solutions to address these challenges.

Benefits

Qualifications

• We are seeking a highly skilled and experienced Lead AI Engineer to join our dynamic team.
• Bachelor's degree in computer science, Data Science, Machine Learning, AI, or related fields; advanced degree strongly preferred.
• 5+ years of proven experience developing and deploying production-grade AI/ML systems.
• Strong programming
• Master's or Ph.
• in Computer Science, Machine Learning, Artificial Intelligence, or related field.
• Familiarity with cloud-based AI services (e.g., AWS SageMaker, Azure ML, Google Vertex AI).
• Experience with containerization (Docker, Kubernetes) and deployment pipelines (CI/CD).
• Knowledge of advanced AI frameworks and model inference engines such as Triton Inference Server, TensorRT, and ONNX.
• Familiarity with model monitoring, observability
• Granite delivers advanced communications and technology solutions to businesses and government agencies throughout the United States and Canada.
• We provide exceptional customized service with an emphasis on reliability and outstanding customer support and our customers include over 85 of the Fortune 100.

Responsibilities

• will include designing, building, and deploying enterprise-grade AI systems, specifically focused on:
• Agentic AI solutions to automate operational processes (e.g., interpreting trouble tickets, performing basic troubleshooting, interacting with online portals, data entry).
• Retrieval-Augmented Generation (RAG) and ColBERTv2 pipelines for parsing, indexing, and querying enterprise documents to facilitate answers related to process guidelines, product knowledge, and training materials.
• Function calling solutions leveraging Large Language Models (LLMs) to automate and perform precise actions in enterprise workflows.
• Developing and applying reinforcement learning strategies to optimize and automate decision-making processes within enterprise operations.
• Develop and implement AI solutions leveraging fine-tuned Large Language Models (e.g., OpenAI models, LLaMA, Mistral).
• Design, develop, and optimize Retrieval-Augmented Generation (RAG) pipelines using advanced vector databases (e.g., FAISS, Pinecone, Milvus).
• Build and enhance agentic AI systems utilizing frameworks like LangChain, AutoGPT, or similar automation frameworks.
• Deploy scalable ColBERTv2 architectures for semantic retrieval and classification.
• Create robust pre-processing and post-processing pipelines to enhance model performance, accuracy, and interpretability.
• Collaborate closely with cross-functional teams, including product managers, business stakeholders, data scientists, and software engineers.
• Implement best practices in model distillation, quantization, and optimization for deployment in production environments.

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