AI Native Architect (Python + GenAI + Cloud)
Summary
Designs and delivers enterprise-grade AI solutions — LLMs, RAG pipelines, agentic multi-agent workflows, and cloud-native AI infrastructure — while leading architecture decisions, mentoring engineers, and supporting client presales. Core stack: Python, LangChain/LangGraph, vector databases, MLOps/LLMOps, and Azure/AWS/GCP AI services.
We are looking for an experienced AI Native Architect to design, architect and deliver scalable, production-grade AI solutions using Python, Generative AI, LLMs, RAG and Agentic AI. The role involves defining enterprise AI architecture, leading technical delivery, and building secure, reliable and cloud-native intelligent systems.
Key Responsibilities
- Design end-to-end AI-native architectures with LLMs, RAG, agentic workflows and multi-model orchestration.
- Build and optimize RAG pipelines, embeddings, vector search, prompt engineering and LLM-powered applications.
- Design multi-agent systems using LangChain, LangGraph, AutoGen, CrewAI, Semantic Kernel or equivalent frameworks.
- Define cloud architecture across Azure, AWS or GCP, including model deployment, vector stores and AI infrastructure.
- Establish MLOps/LLMOps practices covering CI/CD, evaluation, monitoring, observability, cost and performance optimization.
- Ensure AI security, data privacy, responsible AI and compliance requirements, including HIPAA where applicable.
- Lead architecture decisions, mentor engineering teams, engage with clients and support solution design, estimations and presales.
Required Skills & Experience
- 10+ years of overall technology experience, with 4 to 5 years of substantial AI/ML architecture and delivery experience preferred.
- Strong hands-on expertise in Python, Generative AI, LLMs, RAG, embeddings, vector databases and prompt engineering.
- Practical experience designing and delivering production-grade Agentic AI / multi-agent solutions.
- Knowledge of traditional ML, model evaluation and frameworks such as PyTorch, TensorFlow, Hugging Face or scikit-learn.
- Experience with Azure OpenAI, AWS Bedrock, Vertex AI or equivalent cloud AI services.
- Familiarity with Docker, Kubernetes, CI/CD, MLOps/LLMOps and AI monitoring tools.
- Strong architecture fundamentals in APIs, microservices, event-driven systems, scalability and security.
- Excellent communication, technical leadership and stakeholder management skills.
Preferred Qualifications
- Experience in Retail or US Healthcare, including payer/provider or life sciences.
- Knowledge of HIPAA, responsible AI, model governance and explainability.
- Prior experience in consulting, IT services, client-facing architecture or technical leadership.
- Bachelor's or Master's degree in Computer Science, IT, Engineering, Data Science or a related field.
- Relevant Azure, AWS or GCP certifications are an advantage.
Skills
- Agentic AI
- AI
- API
- AutoGen
- AWS
- AWS Bedrock
- Azure
- CI/CD
- Cloud
- Cloud Native
- CrewAI
- Data Science
- Docker
- Embeddings
- Event Driven Architecture
- GCP
- Generative AI
- Hipaa
- Hugging Face
- Kubernetes
- LangChain
- LangGraph
- LLM
- LLMOps
- Machine Learning
- Microservices
- MLOps
- Model Deployment
- Model Evaluation
- Observability
- OpenAI
- Pre-sales
- Prompt Engineering
- Python
- PyTorch
- RAG
- scikit-learn
- Semantic Kernel
- Solution Design
- Stakeholder Management
- TensorFlow
- Vector Databases
- Vector Search
- Vertex AI
