Artificial Intelligence Architect

Job Summary:

AI Architect

Key Responsibilities:

Architecture & Strategy

• Design end-to-end AI architectures that include data pipelines, model development, MLOps, and inference serving.
• Create scalable, reusable, and modular AI components for different use cases (vision, NLP, time series, etc.).
• Drive architecture decisions across AI solutions, including multi-modal models, LLMs, and agentic workflows.
• Ensure interoperability of AI systems across cloud (AWS/GCP/Azure), edge, and hybrid environments.

Technical Leadership

• Guide teams in selecting appropriate models (traditional ML, deep learning, transformers, etc.) and technologies.
• Lead architectural reviews and ensure compliance with security, performance, and governance policies.
• Mentor engineering and data science teams in best practices for AI/ML, GenAI, and MLOps.

Model Lifecycle & Engineering

• Oversee implementation of model lifecycle using CI/CD for ML (MLOps) and/or LLMOps workflows.
• Define architecture for Retrieval Augmented Generation (RAG), vector databases, embeddings, prompt engineering, etc.
• Design pipelines for fine-tuning, evaluation, monitoring, and retraining of models.

Data & Infrastructure

• Collaborate with data engineers to ensure data quality, feature pipelines, and scalable data stores.
• Architect systems for synthetic data generation, augmentation, and real-time streaming inputs.
• Define solutions leveraging data lakes, data warehouses, and graph databases.

Client Engagement / Product Integration

• Interface with business/product stakeholders to align AI strategy with KPIs.
• Collaborate with DevOps teams to integrate models into products via APIs/microservices.

Required Skills & Experience:

Core Skills

• Strong foundation in AI/ML/DL (Scikit-learn, TensorFlow, PyTorch, Transformers, Langchain, etc.)
• Advanced knowledge of Generative AI (LLMs, diffusion models, multimodal models, etc.)
• Proficiency in cloud-native architectures (AWS/GCP/Azure) and containerization (Docker, Kubernetes)
• Experience with orchestration frameworks (Airflow, Ray, LangGraph, or similar)
• Familiarity with vector databases (Weaviate, Pinecone, FAISS), LLMOps platforms, and RAG design

Architecture & Programming

• Solid experience in architectural patterns (microservices, event-driven, serverless)
• Proficient in Python and optionally Java/Go
• Knowledge of APIs (REST, GraphQL), streaming (Kafka), and observability tooling (Prometheus, ELK, Grafana)

Tools & Platforms

• ML lifecycle tools: MLflow, Kubeflow, Vertex AI, Sagemaker, Hugging Face, etc.
• Prompt orchestration tools: LangChain, CrewAI, Semantic Kernel, DSPy (nice to have)
• Knowledge of security, privacy, and compliance (GDPR, SOC2, HIPAA, etc.)