[Remote] AI Architect (Growth Lead)

Note: The job is a remote job and is open to candidates in USA. Tredence Inc. 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-ready AI solutions, not just prototypes, ensuring scalability, reliability, and observability
• Develop reusable frameworks, accelerators, and reference architectures for enterprise AI adoption
• Architect and deploy 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:
• Reflection and planning agents
• Retrieval + reasoning hybrid systems
• Autonomous pipelines for analytics and operations
• Work hands-on with:
• Frameworks: LangChain, LlamaIndex, Semantic Kernel, AutoGen, CrewAI
• Models: OpenAI, Claude, open-source LLMs (Llama, Mistral, etc.)
• Vector DBs: Pinecone, Weaviate, FAISS, Azure AI Search
• Build and optimize:
• Prompt engineering strategies
• Fine-tuning and adaptation (LoRA, PEFT where applicable)
• Latency, cost, and inference optimization
• Implement evaluation pipelines (hallucination detection, grounding accuracy, guardrails)
• Architect and deploy solutions on:
• Azure OpenAI, AWS Bedrock, Google 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 ideas
• 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 proof-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-ready AI solutions ensuring scalability, reliability, and observability
• Experience in developing reusable frameworks, accelerators, and reference architectures for enterprise 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 reflection and planning agents, retrieval + reasoning hybrid systems, and autonomous pipelines for analytics and operations
• Hands-on experience with frameworks such as LangChain, LlamaIndex, Semantic Kernel, AutoGen, CrewAI
• Experience with models like OpenAI, Claude, and open-source LLMs (Llama, Mistral, etc.)
• Knowledge of vector databases such as Pinecone, Weaviate, FAISS, Azure AI Search
• Ability to build and optimize prompt engineering strategies
• Experience in fine-tuning and adaptation (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 OpenAI, AWS Bedrock, Google 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 ideas
• 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

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