Kaizen Analytix
AI Engineer (Generative AI, LLMs, AIOps)
Kaizen Analytix, Atlanta, Georgia, United States, 30383
Overview
AI Engineer (Generative AI, LLMs, AIOps) role at Kaizen Analytix. Kaizen Analytix LLC, an analytics products and services company that gives clients unmatched speed to value through AI/ML solutions and actionable business insights, is seeking qualified candidates for AI Engineer who are highly skilled and experienced professionals responsible for designing, developing, and maintaining complex AI projects and managed data warehouses for hosting large datasets used in the models. The ideal candidate will have a strong understanding of deep learning, vector embeddings, and resolving the challenges of storing the embeddings; and using advanced data engineering principles and best practices, as well as working with massive datasets (100 GB+) that are unstructured, like video, audio, images, and text. We seek candidates who can support AI projects with the requisite knowledge on Deep learning, Embeddings, Data engineering skills required for storing Deep learning-based outcomes.
Responsibilities
Hands-on Development & Implementation: Design, build, fine-tune, and deploy generative models and LLMs for various enterprise applications (e.g., content generation, chatbots, code assistance, data analysis).
Leverage deep knowledge of transformer architectures (e.g., GPT, BERT, T5) to fine-tune, optimize, and deploy Large Language Models (LLMs) for specific tasks.
Implement state-of-the-art techniques such as Agentic AI, Context Engineering - Retrieval-Augmented Generation (RAG), prompt engineering, and model quantization.
Build, train, and deploy autonomous and semi-autonomous AI agents capable of complex reasoning, tool use, and decision-making.
Experience with document extraction models for information retrieval using Document Intelligence or Textract cloud services.
Develop and implement AI/ML models for AIOps use cases, including anomaly detection, predictive monitoring, root cause analysis, and automated remediation.
Write clean, efficient, well-documented, production-ready Python code.
Build and maintain data pipelines for training, evaluating, and serving AI models.
AI Architecture & Design: Design end-to-end architectures for complex AI applications with focus on scalability, reliability, security, maintainability, and cost-effectiveness in an enterprise environment.
Evaluate and select AI/ML frameworks, models, platforms, and tools for specific projects.
Collaborate with data scientists, software engineers, DevOps, product managers, and business stakeholders to translate requirements into technical designs.
Develop and advocate for best practices in AI development, deployment (MLOps), and governance.
Deep Learning & Research: Understand fundamental deep learning concepts (e.g., CNNs, RNNs, LSTMs, Transformers, attention mechanisms) to solve complex problems.
Stay current with advancements in Gen AI, LLMs, AIOps, and deep learning.
Experiment with new algorithms, techniques, and tools to drive innovation.
AIOps Integration: Integrate AI capabilities into IT operations monitoring, logging, and management tools.
Analyze operational data (logs, metrics, traces) to identify opportunities for AI-driven improvements.
Develop systems to automate operational tasks and improve system resilience using AI.
Required Qualifications
Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or related quantitative field. PhD is a plus.
3-5+ years of AI/ML engineering experience with hands-on experience building and deploying machine learning models in production.
Proven experience with Generative AI and Large Language Models (e.g., GPT variants, Llama, Mistral, Gemini), including fine-tuning, RAG, and prompt engineering.
Strong proficiency in Python and AI/ML libraries/frameworks (TensorFlow, PyTorch, Keras, Scikit-learn, Hugging Face Transformers, LangChain).
Solid understanding of core deep learning concepts, model architectures, training methodologies, and evaluation metrics.
Experience designing scalable, reliable AI/ML system architectures.
Familiarity with cloud platforms (AWS, Azure, or GCP) and their AI/ML services (SageMaker, Azure ML, Vertex AI, etc.).
Understanding of AIOps principles and experience implementing AIOps solutions is a strong plus.
Solid software engineering fundamentals (data structures, algorithms, code quality, testing, Git).
Excellent analytical, problem-solving, and communication skills.
Good-to-have Qualifications
Hands-on experience developing and deploying AIOps solutions.
MLOps tools and practices (MLflow, Kubeflow, DVC, CI/CD for ML).
Willingness to learn new tools and solve adhoc challenges.
Containerization (Docker, Kubernetes).
Big data technologies (Spark, Hadoop, Kafka).
Contributions to open-source AI/ML projects or publications.
Experience working in an agile development environment.
This is a remote role.
Analysis and Design
Conducts fact-gathering sessions with users.
Consult with Technical Managers and Business Owners to identify and analyze technological needs and problems.
Perform data flow diagramming and/or process modeling (code architecture).
Design, develop, and deploy ML models for tasks such as classification, image recognition, NLP, and anomaly detection.
Evaluate model performance and tune for optimal results.
Design and implement scalable, robust MLOps.
Collect, clean, and prepare data for ML models.
Collaborate with data scientists, engineers, and stakeholders to prioritize ML projects.
Stay up to date on ML research and best practices.
Work with stakeholders to gather requirements and define data models.
Troubleshoot data issues and performance problems.
Maintain the company’s data infrastructure and stay current with data engineering trends, including vector stores.
Strategy Alignment
Collaborate with technical team members to improve implementation strategies, standards, and documentation.
Provide technical assistance and mentoring.
Communicate plans, status, and issues to management.
Adhere to department standards and best practices.
Additional Details
Seniority level: Mid-Senior level
Employment type: Full-time
Job function: Sales, General Business, and Education
Industries: Wireless Services, Telecommunications, and Communications Equipment Manufacturing
Note: This description reflects the job posting content and does not include extraneous postings or unrelated items.
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AI Engineer (Generative AI, LLMs, AIOps) role at Kaizen Analytix. Kaizen Analytix LLC, an analytics products and services company that gives clients unmatched speed to value through AI/ML solutions and actionable business insights, is seeking qualified candidates for AI Engineer who are highly skilled and experienced professionals responsible for designing, developing, and maintaining complex AI projects and managed data warehouses for hosting large datasets used in the models. The ideal candidate will have a strong understanding of deep learning, vector embeddings, and resolving the challenges of storing the embeddings; and using advanced data engineering principles and best practices, as well as working with massive datasets (100 GB+) that are unstructured, like video, audio, images, and text. We seek candidates who can support AI projects with the requisite knowledge on Deep learning, Embeddings, Data engineering skills required for storing Deep learning-based outcomes.
Responsibilities
Hands-on Development & Implementation: Design, build, fine-tune, and deploy generative models and LLMs for various enterprise applications (e.g., content generation, chatbots, code assistance, data analysis).
Leverage deep knowledge of transformer architectures (e.g., GPT, BERT, T5) to fine-tune, optimize, and deploy Large Language Models (LLMs) for specific tasks.
Implement state-of-the-art techniques such as Agentic AI, Context Engineering - Retrieval-Augmented Generation (RAG), prompt engineering, and model quantization.
Build, train, and deploy autonomous and semi-autonomous AI agents capable of complex reasoning, tool use, and decision-making.
Experience with document extraction models for information retrieval using Document Intelligence or Textract cloud services.
Develop and implement AI/ML models for AIOps use cases, including anomaly detection, predictive monitoring, root cause analysis, and automated remediation.
Write clean, efficient, well-documented, production-ready Python code.
Build and maintain data pipelines for training, evaluating, and serving AI models.
AI Architecture & Design: Design end-to-end architectures for complex AI applications with focus on scalability, reliability, security, maintainability, and cost-effectiveness in an enterprise environment.
Evaluate and select AI/ML frameworks, models, platforms, and tools for specific projects.
Collaborate with data scientists, software engineers, DevOps, product managers, and business stakeholders to translate requirements into technical designs.
Develop and advocate for best practices in AI development, deployment (MLOps), and governance.
Deep Learning & Research: Understand fundamental deep learning concepts (e.g., CNNs, RNNs, LSTMs, Transformers, attention mechanisms) to solve complex problems.
Stay current with advancements in Gen AI, LLMs, AIOps, and deep learning.
Experiment with new algorithms, techniques, and tools to drive innovation.
AIOps Integration: Integrate AI capabilities into IT operations monitoring, logging, and management tools.
Analyze operational data (logs, metrics, traces) to identify opportunities for AI-driven improvements.
Develop systems to automate operational tasks and improve system resilience using AI.
Required Qualifications
Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or related quantitative field. PhD is a plus.
3-5+ years of AI/ML engineering experience with hands-on experience building and deploying machine learning models in production.
Proven experience with Generative AI and Large Language Models (e.g., GPT variants, Llama, Mistral, Gemini), including fine-tuning, RAG, and prompt engineering.
Strong proficiency in Python and AI/ML libraries/frameworks (TensorFlow, PyTorch, Keras, Scikit-learn, Hugging Face Transformers, LangChain).
Solid understanding of core deep learning concepts, model architectures, training methodologies, and evaluation metrics.
Experience designing scalable, reliable AI/ML system architectures.
Familiarity with cloud platforms (AWS, Azure, or GCP) and their AI/ML services (SageMaker, Azure ML, Vertex AI, etc.).
Understanding of AIOps principles and experience implementing AIOps solutions is a strong plus.
Solid software engineering fundamentals (data structures, algorithms, code quality, testing, Git).
Excellent analytical, problem-solving, and communication skills.
Good-to-have Qualifications
Hands-on experience developing and deploying AIOps solutions.
MLOps tools and practices (MLflow, Kubeflow, DVC, CI/CD for ML).
Willingness to learn new tools and solve adhoc challenges.
Containerization (Docker, Kubernetes).
Big data technologies (Spark, Hadoop, Kafka).
Contributions to open-source AI/ML projects or publications.
Experience working in an agile development environment.
This is a remote role.
Analysis and Design
Conducts fact-gathering sessions with users.
Consult with Technical Managers and Business Owners to identify and analyze technological needs and problems.
Perform data flow diagramming and/or process modeling (code architecture).
Design, develop, and deploy ML models for tasks such as classification, image recognition, NLP, and anomaly detection.
Evaluate model performance and tune for optimal results.
Design and implement scalable, robust MLOps.
Collect, clean, and prepare data for ML models.
Collaborate with data scientists, engineers, and stakeholders to prioritize ML projects.
Stay up to date on ML research and best practices.
Work with stakeholders to gather requirements and define data models.
Troubleshoot data issues and performance problems.
Maintain the company’s data infrastructure and stay current with data engineering trends, including vector stores.
Strategy Alignment
Collaborate with technical team members to improve implementation strategies, standards, and documentation.
Provide technical assistance and mentoring.
Communicate plans, status, and issues to management.
Adhere to department standards and best practices.
Additional Details
Seniority level: Mid-Senior level
Employment type: Full-time
Job function: Sales, General Business, and Education
Industries: Wireless Services, Telecommunications, and Communications Equipment Manufacturing
Note: This description reflects the job posting content and does not include extraneous postings or unrelated items.
#J-18808-Ljbffr