About the Role
We are seeking a highly experienced and visionary AI Architect to lead the design, development, and governance of enterprise-scale AI and machine learning solutions. In this role, you will define the technical direction for AI/ML platforms, oversee the adoption of Large Language Models (LLMs) and Agentic AI systems, and collaborate with cross-functional teams to deliver intelligent, scalable, and responsible AI solutions aligned with business objectives.
Technical Skills Summary
Category: Skills
Languages: Python, SQL, Scala, R
ML Frameworks: PyTorch, TensorFlow, Scikit-learn, Hugging Face, JAX
LLM / GenAI: GPT-4, Claude, LLaMA, Mistral, Gemini, RLHF, LoRA
Agentic AI: LangChain, LangGraph, AutoGen, CrewAI, Semantic Kernel
MLOps: MLflow, Kubeflow, SageMaker, Vertex AI, Azure ML
Cloud Platform(s): AWS, Azure, GCP
Vector Database(s): Pinecone, ChromaDB, FAISS, Weaviate
Data Engineering: Spark, Kafka, dbt, Airflow
DevOps/Infra: Docker, Kubernetes, Terraform, CI/CD
Key Responsibilities
Architecture & Design
- Define and own the enterprise AI/ML architecture strategy, including model development pipelines, MLOps platforms, and LLM integration patterns
- Design scalable, secure, and maintainable AI systems leveraging cloud-native services (AWS, Azure, GCP)
- Architect Retrieval-Augmented Generation (RAG) systems, vector database solutions, and knowledge graph integrations
- Establish architectural patterns for Agentic AI systems including multi-agent orchestration, tool use, memory management, and autonomous workflows
- Lead technical design reviews and ensure alignment with enterprise standards, security policies, and compliance requirements
LLM & Generative AI
- Evaluate, select, and integrate LLMs (e.g., GPT-4, Claude, Gemini, LLaMA, Mistral) for enterprise use cases
- Architect fine-tuning pipelines (LoRA, QLoRA, PEFT) for domain-specific model adaptation
- Define prompt engineering standards, guardrails, and output validation frameworks
- Oversee responsible AI practices including bias detection, hallucination mitigation, and explainability
MLOps & Platform Engineering
- Design end-to-end MLOps pipelines covering data ingestion, model training, evaluation, deployment, monitoring, and retraining
- Establish CI/CD practices for ML models and AI applications
- Define model registry, versioning, and governance standards
- Select and integrate ML platforms (e.g., MLflow, Kubeflow, SageMaker, Azure ML, Vertex AI)
Agentic AI Systems
- Architect multi-agent frameworks using tools such as LangGraph, AutoGen, CrewAI, and Semantic Kernel
- Define agent orchestration patterns, tool-use boundaries, and human-in-the-loop approval workflows
- Establish security controls for agentic systems including prompt injection prevention and privilege separation
- Drive adoption of Model Context Protocol (MCP) and emerging agentic standards
Leadership & Collaboration
- Serve as the technical authority and subject matter expert for AI/ML
- Mentor and guide a team of ML engineers, data scientists, and AI developers
- Partner with product, data, security, and business stakeholders to translate requirements into AI solutions
- Present architectural decisions, trade-offs, and roadmaps to executive leadership
- Stay current with AI research, emerging frameworks, and industry trends; drive continuous innovation
Required Qualifications
- Education: Bachelor's or Master's degree in Computer Science, Data Science, Electrical Engineering, or a related field
- Experience: 10+ years in software engineering or data science; 5+ years in AI/ML architecture roles
- Deep expertise in machine learning, deep learning, and statistical modeling
- Hands-on experience with LLMs (GPT, Claude, LLaMA, Mistral) and generative AI application development
- Strong proficiency in Python; experience with TensorFlow, PyTorch, Scikit-learn, and Hugging Face
- Solid understanding of Transformer architecture, attention mechanisms, and NLP fundamentals
- Experience designing RAG pipelines with vector databases (Pinecone, ChromaDB, Weaviate, FAISS)
- Proficiency with cloud AI services on AWS (SageMaker, Bedrock), Azure (OpenAI, ML Studio), or GCP (Vertex AI)
- Strong knowledge of MLOps practices: MLflow, Kubeflow, model monitoring, feature stores
- Familiarity with agentic AI frameworks: LangChain, LangGraph, AutoGen, CrewAI, Semantic Kernel
- Experience with containerization and orchestration: Docker, Kubernetes
- Understanding of data engineering principles: ETL, data lakes, streaming pipelines (Kafka, Spark)
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