Senior AI Architect
Role: Senior AI Architect (ML / MLOps / GenAI)
Location: Atlanta, GA 30342 (100% Onsite)
We are seeking a Senior AI Architect with deep expertise in Generative AI, Large Language Models (LLMs), ML models, MLOps, AI Governance, and scalable AI architectures. This role requires hands-on experience in building, optimizing, and deploying AI/ML solutions while driving end-to-end model lifecycle management. The ideal candidate should have very strong knowledge of vector databases, chatbot architectures, hyperparameter tuning, statistics, ML model optimization, and AI security. A superior person with all-rounder experience in leading AI/ML, ML OPS and Governance delivery teams mixed of data scientists, data engineers as well as AI/ML Engineers.
Key Responsibilities:
- End-to-End ML Development & Model Optimization
• Design, develop, and deploy ML models including Random Forest, XGBoost, Light GBM, SVMs, and Deep Learning models (CNN, RNN, Transformers).
• Perform hyperparameter tuning using Grid Search, Bayesian Optimization, Genetic Algorithms, and Reinforcement Learning.
• Implement advanced feature engineering, feature selection, and dimensionality reduction techniques (PCA, LDA).
• Optimize model inference latency, throughput, and memory footprint for real-time applications.
- Generative AI & LLM Development
• Fine-tune and optimize LLMs (GPT, Claude, Llama, Falcon, Mistral) for chatbots, document processing, content generation, and AI agents.
• Architect retrieval-augmented generation (RAG) pipelines using vector databases (FAISS, Pinecone, ChromaDB, Milvus).
• Implement prompt engineering, chain-of-thought reasoning, and context-aware AI solutions.
• Develop multi-modal AI applications, integrating text, image, and speech models.
- MLOps & Model Lifecycle Management
• Build CI/CD pipelines for ML workflows using MLflow, TFX, SageMaker Pipelines, or Kubeflow.
• Monitor and mitigate model drift through A/B testing, retraining pipelines, and performance tracking (Evidently AI, SHAP, LIME).
• Deploy models using containerized solutions (Docker, Kubernetes, AWS ECS/Fargate, Lambda).
• Optimize inference using TensorRT, ONNX, quantization, and model pruning for cost-efficient AI solutions.
- AI Governance, Statistics & Model Explainability
• Implement AI governance best practices, ensuring compliance with GDPR, AI Act, HIPAA, and other regulations.
• Apply statistical techniques (hypothesis testing, probability distributions, regression analysis) for model validation and bias detection.
• Utilize explainability tools (SHAP, LIME, Integrated Gradients, Captum) for transparent AI models.
- API Development & Performance Optimization
• Design and deploy high-performance APIs (FastAPI, Flask, GraphQL) for AI model integration.
• Optimize API latency, caching, async processing, and load balancing to support real-time AI systems.
- Leadership, Collaboration & Innovation
• Partner with Data Engineers, Cloud Architects, and Product Teams to align AI/ML solutions with business goals.
• Mentor teams, lead technical discussions, and drive innovation in AI/ML technologies.
• Stay ahead of emerging trends in AI/ML, including LLM advancements, reinforcement learning, and AI security.
• Lead by example and help the client in crucial decision-making situations
• Go Getter for a technical delivery team with superior knowledge around data science and AI/ML, ML OPS and governance
Technical Skills & Expertise:
• Machine Learning & AI: XGBoost, Random Forest, SVMs, CNNs, RNNs, Transformers, LLMs (GPT, Claude, Llama, Falcon).
• Hyperparameter Tuning & Optimization: Grid Search, Bayesian Optimization, Genetic Algorithms, Reinforcement Learning.
• Generative AI & NLP: LangChain, Prompt Engineering, RAG, FAISS, Pinecone, Vector Search, Embeddings.
• Statistics & Data Science: Hypothesis Testing, Regression Analysis, Probability, Feature Engineering, Dimensionality Reduction.
• MLOps & Deployment: MLflow, TFX, Kubeflow, SageMaker Pipelines, Docker, Kubernetes, CI/CD Pipelines.
• Cloud & Infrastructure: AWS (SageMaker, Lambda, API Gateway, S3, EKS, CloudFormation), Terraform, CDK.
• API & Performance Optimization: FastAPI, Flask, GraphQL, Async Processing, Caching.
• AI Governance & Compliance: Bias detection, Explainability (SHAP, LIME), Model Drift, AI Security & Compliance.
• Data Science technology knowledge: Technologies such as Python, PySpark, Scala, Data Lake, Data Warehouses would be a plus.
Role: Senior AI Architect (ML / MLOps / GenAI)
Location: Atlanta, GA 30342 (100% Onsite)
We are seeking a Senior AI Architect with deep expertise in Generative AI, Large Language Models (LLMs), ML models, MLOps, AI Governance, and scalable AI architectures. This role requires hands-on experience in building, optimizing, and deploying AI/ML solutions while driving end-to-end model lifecycle management. The ideal candidate should have very strong knowledge of vector databases, chatbot architectures, hyperparameter tuning, statistics, ML model optimization, and AI security. A superior person with all-rounder experience in leading AI/ML, ML OPS and Governance delivery teams mixed of data scientists, data engineers as well as AI/ML Engineers.
Key Responsibilities:
- End-to-End ML Development & Model Optimization
• Design, develop, and deploy ML models including Random Forest, XGBoost, Light GBM, SVMs, and Deep Learning models (CNN, RNN, Transformers).
• Perform hyperparameter tuning using Grid Search, Bayesian Optimization, Genetic Algorithms, and Reinforcement Learning.
• Implement advanced feature engineering, feature selection, and dimensionality reduction techniques (PCA, LDA).
• Optimize model inference latency, throughput, and memory footprint for real-time applications.
- Generative AI & LLM Development
• Fine-tune and optimize LLMs (GPT, Claude, Llama, Falcon, Mistral) for chatbots, document processing, content generation, and AI agents.
• Architect retrieval-augmented generation (RAG) pipelines using vector databases (FAISS, Pinecone, ChromaDB, Milvus).
• Implement prompt engineering, chain-of-thought reasoning, and context-aware AI solutions.
• Develop multi-modal AI applications, integrating text, image, and speech models.
- MLOps & Model Lifecycle Management
• Build CI/CD pipelines for ML workflows using MLflow, TFX, SageMaker Pipelines, or Kubeflow.
• Monitor and mitigate model drift through A/B testing, retraining pipelines, and performance tracking (Evidently AI, SHAP, LIME).
• Deploy models using containerized solutions (Docker, Kubernetes, AWS ECS/Fargate, Lambda).
• Optimize inference using TensorRT, ONNX, quantization, and model pruning for cost-efficient AI solutions.
- AI Governance, Statistics & Model Explainability
• Implement AI governance best practices, ensuring compliance with GDPR, AI Act, HIPAA, and other regulations.
• Apply statistical techniques (hypothesis testing, probability distributions, regression analysis) for model validation and bias detection.
• Utilize explainability tools (SHAP, LIME, Integrated Gradients, Captum) for transparent AI models.
- API Development & Performance Optimization
• Design and deploy high-performance APIs (FastAPI, Flask, GraphQL) for AI model integration.
• Optimize API latency, caching, async processing, and load balancing to support real-time AI systems.
- Leadership, Collaboration & Innovation
• Partner with Data Engineers, Cloud Architects, and Product Teams to align AI/ML solutions with business goals.
• Mentor teams, lead technical discussions, and drive innovation in AI/ML technologies.
• Stay ahead of emerging trends in AI/ML, including LLM advancements, reinforcement learning, and AI security.
• Lead by example and help the client in crucial decision-making situations
• Go Getter for a technical delivery team with superior knowledge around data science and AI/ML, ML OPS and governance
Technical Skills & Expertise:
• Machine Learning & AI: XGBoost, Random Forest, SVMs, CNNs, RNNs, Transformers, LLMs (GPT, Claude, Llama, Falcon).
• Hyperparameter Tuning & Optimization: Grid Search, Bayesian Optimization, Genetic Algorithms, Reinforcement Learning.
• Generative AI & NLP: LangChain, Prompt Engineering, RAG, FAISS, Pinecone, Vector Search, Embeddings.
• Statistics & Data Science: Hypothesis Testing, Regression Analysis, Probability, Feature Engineering, Dimensionality Reduction.
• MLOps & Deployment: MLflow, TFX, Kubeflow, SageMaker Pipelines, Docker, Kubernetes, CI/CD Pipelines.
• Cloud & Infrastructure: AWS (SageMaker, Lambda, API Gateway, S3, EKS, CloudFormation), Terraform, CDK.
• API & Performance Optimization: FastAPI, Flask, GraphQL, Async Processing, Caching.
• AI Governance & Compliance: Bias detection, Explainability (SHAP, LIME), Model Drift, AI Security & Compliance.
• Data Science technology knowledge: Technologies such as Python, PySpark, Scala, Data Lake, Data Warehouses would be a plus.
