About the Job/Role

We are seeking a Senior AI/ML Architect to lead the design and implementation of AI-first systems that integrate LLMs, RAG pipelines, and agentic workflows. This role combines deep technical expertise with strategic architectural leadership, enabling rapid deployment of scalable, secure, and high-performance GenAI solutions. You will drive innovation, mentor teams, and influence product and infrastructure decisions to deliver business impact at scale.

Roles & Responsibilities

Lead architectural design of AI/ML systems from concept to deployment, including LLMs, RAG pipelines, and agentic workflows.
Design scalable, cloud-native infrastructure for secure, low-latency GenAI deployments.
Define AI platform strategies, including model selection, orchestration layers, data pipelines, and inference stacks.
Architect modular APIs and reusable GenAI components across teams and products.
Collaborate with engineering, product, and data science teams to deploy AI capabilities rapidly into production.
Evaluate, implement, and optimize LLMs (e.g., GPT, Claude, LLaMA) and vector databases (e.g., Pinecone, Qdrant).
Champion best practices in AI safety, model governance, and performance monitoring.
Solve complex system design problems and provide technical mentorship to elevate team capabilities.

Required Qualifications

10+ years in software architecture and engineering, including 3–5 years in AI/ML systems.
Hands-on experience with LLM APIs (OpenAI, Anthropic, Mistral) and GenAI tools (LangChain, LlamaIndex, Temporal, AutoGen).
Proven track record of architecting production-grade GenAI or ML-powered products.
Expertise in system design, microservices, API architecture, and distributed systems.
Proficiency in Python and at least one backend/system-level language (.NET, Java, etc.).
Strong understanding of model training pipelines, retrieval systems, embeddings, and cloud AI infrastructure.
Familiarity with MLOps, Kubernetes, and CI/CD for AI model deployment.

Preferred Qualifications:

Experience with multi-modal AI, agentic systems, or domain-specific fine-tuning.
Knowledge of AI regulations, ethical AI frameworks, and prompt security.
Contributions to AI/ML open source or published research.
Ability to mentor cross-functional teams and lead architecture councils.

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