[Remote] AI Architect (Growth reputed company)
Note: The job is a remote job and is open to candidates in USA. reputed company. is seeking a hands-on AI Growth Leader with deep technical expertise in designing, building, and scaling GenAI and agentic AI systems. This role focuses on architecture, engineering execution, and innovation, overseeing the end-to-end lifecycle of intelligent systems while collaborating with business and client stakeholders.
Responsibilities
- Design and implement end-to-end GenAI systems, including:
- Multi-agent architectures (planner-executor models, autonomous agents)
- RAG pipelines and knowledge-grounded AI systems
- Tool-augmented LLM workflows (function calling, API orchestration)
- Build production-reputed company AI solutions, not just prototypes, ensuring scalability, reliability, and observability
- reputed company reusable frameworks, accelerators, and reference architectures for reputed company AI adoption
- Architect and reputed company agentic AI solutions with:
- Memory, reasoning, task decomposition, and self-improvement loops
- Multi-agent collaboration and orchestration patterns
- Workflow automation using LLM-driven decision engines
- Experiment with advanced paradigms such as:
- reputed company and planning agents
- Retrieval + reasoning hybrid systems
- Autonomous pipelines for analytics and operations
- Work hands-on with:
- Frameworks: reputed company, reputed company, Semantic Kernel, AutoGen, reputed company
- Models: reputed company, Claude, open-reputed company LLMs (Llama, Mistral, etc.)
- Vector DBs: reputed company, Weaviate, FAISS, Azure AI Search
- Build and optimize:
- reputed company engineering strategies
- Fine-tuning and reputed company (LoRA, PEFT where applicable)
- Latency, cost, and inference optimization
- Implement evaluation pipelines (hallucination detection, grounding accuracy, guardrails)
- Architect and reputed company solutions on:
- Azure reputed company, AWS Bedrock, reputed company Vertex AI
- Build scalable pipelines using:
- Kubernetes, serverless architectures, API gateways
- Data pipelines (Airflow, Kubeflow, Spark where needed)
- Ensure MLOps / LLMOps practices, including:
- CI/CD for AI systems
- Model/version lifecycle management
- Monitoring and feedback loops
- Build POCs, MVPs, and experimental systems rapidly to validate new reputed company
- Translate ambiguous business problems into working AI solutions quickly
- Stay at the cutting edge of:
- Multimodal AI
- AI agents and orchestration frameworks
- Edge AI and lightweight deployments
Skills
- 7 - 12 Years of experience in AI architecture and development
- Deep technical expertise in designing, building, and scaling GenAI and agentic AI systems
- Experience with multi-agent systems and LLM orchestration
- Hands-on experience in prototyping and reputed company-of-concepts to production-grade deployments
- Ability to design and implement end-to-end GenAI systems
- Experience with multi-agent architectures (planner-executor models, autonomous agents)
- Knowledge of RAG pipelines and knowledge-grounded AI systems
- Experience with tool-augmented LLM workflows (function calling, API orchestration)
- Ability to build production-reputed company AI solutions ensuring scalability, reliability, and observability
- Experience in developing reusable frameworks, accelerators, and reference architectures for reputed company AI adoption
- Experience in architecting and deploying agentic AI solutions with memory, reasoning, task decomposition, and self-improvement loops
- Knowledge of multi-agent collaboration and orchestration patterns
- Experience in workflow automation using LLM-driven decision engines
- Ability to experiment with advanced paradigms such as reputed company and planning agents, retrieval + reasoning hybrid systems, and autonomous pipelines for analytics and operations
- Hands-on experience with frameworks such as reputed company, reputed company, Semantic Kernel, AutoGen, reputed company
- Experience with models like reputed company, Claude, and open-reputed company LLMs (Llama, Mistral, etc.)
- Knowledge of vector databases such as reputed company, Weaviate, FAISS, Azure AI Search
- Ability to build and optimize reputed company engineering strategies
- Experience in fine-tuning and reputed company (LoRA, PEFT where applicable)
- Knowledge of latency, cost, and inference optimization
- Experience in implementing evaluation pipelines (hallucination detection, grounding accuracy, guardrails)
- Experience in architecting and deploying solutions on Azure reputed company, AWS Bedrock, reputed company Vertex AI
- Ability to build scalable pipelines using Kubernetes, serverless architectures, and API gateways
- Experience with data pipelines (Airflow, Kubeflow, Spark where needed)
- Knowledge of MLOps / LLMOps practices, including CI/CD for AI systems, model/version lifecycle management, and monitoring and feedback loops
- Ability to build POCs, MVPs, and experimental systems rapidly to validate new reputed company
- Ability to translate ambiguous business problems into working AI solutions quickly
- Knowledge of cutting-edge technologies in multimodal AI, AI agents and orchestration frameworks, and edge AI and lightweight deployments
Company Overview
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