Senior AI Architect (ML / MLOps / GenAI)

Role: Senior AI Architect (ML / MLOps / GenAI) Location: Atlanta, GA 30342

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:

1. 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.

2. 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.

3. 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.

4. 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.

5. 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.

6. 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.