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, owning the end-to-end lifecycle of intelligent systems and driving real-world impact across automation, decision intelligence, and customer experience.
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 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 production-ready AI solutions ensuring scalability, reliability, and observability
- Experience in architecting and deploying agentic AI solutions
- Experience with cloud-native AI engineering on platforms like Azure OpenAI, AWS Bedrock, Google Vertex AI
- Ability to build scalable data pipelines using tools like Airflow, Kubeflow, Spark
- Experience with MLOps / LLMOps practices including CI/CD for AI systems
- Ability to rapidly prototype and innovate AI solutions
- Strong problem-solving skills to translate ambiguous business problems into working AI solutions
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