Artificial Intelligence Architect

AI Architect Armakuni India

Role Summary:

AI Architect

Key Responsibilities:

• Architectural Leadership

* Define and drive AI/ML architectural vision aligned with business objectives.

* Lead the design of scalable, secure, and cost-optimized AI platforms on AWS or similar clouds.

* Evaluate and select appropriate models, tools, and infrastructure for enterprise deployment.

• Team & Technical Mentorship

* Guide and mentor engineers, data scientists, and MLOps professionals.

* Conduct architecture reviews, enforce coding and deployment standards.

* Foster a culture of experimentation, ownership, and knowledge sharing.

• Generative AI & LLMs

* Architect and deliver GenAI solutions leveraging LLMs like GPT-4, Claude, Gemini, LLaMA, etc.

* Implement agentic workflows, RAG pipelines, and domain-specific LLM fine-tuning.

* Use advanced frameworks like LangChain, LangGraph, LangFuse, Crew AI, LLamaIndex.

• Machine Learning Engineering

* Build robust ML pipelines: preprocessing, model training, evaluation, and drift monitoring.

* Apply classical ML, deep learning, and time series forecasting to solve real-world problems.

* Deploy and manage models using SageMaker and other MLOps tools.

• Computer Vision (CV)

* Architect CV pipelines for image classification, object detection, segmentation, and more.

* Optimize models for cloud and edge deployment using PyTorch/TensorFlow.

• Cloud & Infrastructure

* Lead cloud-native deployments using AWS (SageMaker, Bedrock, Lambda, etc.).

* Use containerization tools (Docker, Kubernetes) for scalable infrastructure.

* Integrate with RESTful APIs, vector databases (Pinecone, FAISS), and caching layers.

• Core Skills & Technologies

* Languages & Libraries: Python, Scikit-learn, XGBoost, PyTorch, TensorFlow

* Generative AI Tools: LangChain, LangGraph, LLamaIndex, LangFuse, Crew AI

* Databases: PostgreSQL, DynamoDB, Redis, Chroma

* MLOps & Deployment: SageMaker, MLflow, FastAPI, Docker, Uvicorn

* Vector Search: Pinecone, FAISS, OpenSearch

* Cloud Platforms: AWS (preferred), GCP, Azure

• Preferred Qualifications

* 8+ years in AI/ML, with 4+ years in GenAI and LLMs

* Proven track record in leading enterprise-grade AI deployments

* Open-source contributions or publications in AI/ML

* AWS AI/ML certification or equivalent is a strong plus