AI Architect

Core Responsibilities

• Architecture & System Design: Own the end-to-end design of AI and GenAI systems from data ingestion and vector indexing to model deployment and inference optimization. Architect scalable LLM/RAG pipelines, multiagent workflows, and generative AI services reusable across client domains. Define enterprise standards for embeddings, prompt orchestration, caching layers, and evaluation pipelines.
• MLOps & Production Deployment: Establish repeatable patterns for fine-tuning and deploying ML/LLM models in production. Drive automation through MLOps and AIOps pipelines using MLflow, Kubeflow, Airflow, and KServe. Architect for multi-cloud scalability across Azure, AWS, and GCP. Build strategic PoCs to validate model fitment and translate business problems into working AI systems.
• Governance, Security & Compliance: Define and enforce AI architecture principles, security policies, and responsible AI guardrails. Implement controls for PII/PHI protection, hallucination risk mitigation, audit logging, and model explainability. Apply zero-trust principles private networking, API gateways, and identity management to keep data within secure perimeters.
• Collaboration & Technical Leadership: Partner with data engineering, cloud, security, and product teams for end to end architectural alignment. Lead build-vs-buy assessments for AI platforms, vector databases, and MLOps tooling. Mentor engineers, conduct architecture reviews, and track the evolving AI landscape to recommend timely adoption of emerging tools.

Technical & Professional Qualifications

• AI Architecture Experience: 8 + years in software/AI engineering, platform engineering, or cloud architecture, with at least 4 years hands-on in production GenAI or LLM systems.
• LLM & Agent Framework Expertise: Deep hands-on experience with LangChain, LlamaIndex, AutoGen, or OpenAI Agents API, with a proven ability to build and deploy multi-agent systems at scale.
• MLOps & Engineering Depth: Strong command of MLflow, Kubeflow, Airflow, KServe, Docker, and Kubernetes. Solid Python skills and distributed systems design experience across Azure, AWS, and GCP.
• Vector & Search Proficiency: Hands-on experience with vector databases Pinecone, FAISS, Chroma DB, Weaviate, or Elasticsearch and strong understanding of RAG patterns and embedding strategies.
• Analytical Thinking: Ability to evaluate foundation model trade-offs, define fine-tuning strategies, and translate complex business problems into scalable AI architectures.
• Governance & Security Knowledge: Strong grasp of data governance, PII/PHI handling, OAuth 2.0, zero-trust architecture, and responsible AI frameworks applicable to enterprise environments.
• Soft Skills: Exceptional ability to communicate architectural decisions to both technical teams and business stakeholders, with a focus on clarity, pragmatism, and long-term system thinking.

Good to Have

• Experience delivering AI solutions in IT services or multi-client consulting environments.
• Hands-on with enterprise platforms such as Salesforce, SAP, or ServiceNow.
• Knowledge of LLMOps, model observability (Datadog, Grafana, OpenTelemetry), and GPU cost optimization.
• Experience with Azure AI Foundry, AWS SageMaker, or GCP Vertex AI at production scale.

Tech Stack

• LLM & Agentic Stack: LangChain/LangGraph, OpenAI/Anthropic APIs, Pydantic AI, Langfuse (observability), and VectorDB (Pinecone/Chroma).
• Deep Learning & Research Stack: Python, PyTorch, Hugging Face Transformers, NumPy.
• Enterprise Production Stack: Python, TensorFlow/Keras, Docker, Kubernetes, AWS SageMaker/Vertex AI.
• Data & Analytics Stack: Apache Spark (Databricks), Pandas, SQL, Kafka