Mid-Senior level
Posted March 26, 2026
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Responsibilities
Responsibilities
- Lead the design and architecture of Generative AI solutions using LLMs
- Build and deploy LLM-powered applications (chatbots, copilots, RAG systems, summarization, etc.)
- Develop agentic AI systems and multi-agent orchestration workflows
- Work with prompt engineering, fine-tuning, and model optimization
- Implement RAG (Retrieval-Augmented Generation) pipelines using vector databases
- Integrate LLMs with enterprise systems via APIs
- Ensure scalability, performance, and security of AI solutions
- Collaborate with cross-functional teams (Data Engineering, DevOps, Product)
- Mentor junior engineers and guide best practices in AI development
- Stay updated with latest advancements in Generative AI Required Skills & Qualifications Core AI/ML
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Requirements
- Ensure scalability, performance, and security of AI solutions
- Collaborate with cross-functional teams (Data Engineering, DevOps, Product)
- Mentor junior engineers and guide best practices in AI development
- Stay updated with latest advancements in Generative AI Required Skills & Qualifications Core AI/ML
- Strong experience in Generative AI & LLMs
- Hands-on with models like:
- OpenAI (GPT)
- Claude
- LLaMA / open-source LLMs Programming
- Expertise in Python
- Experience with AI/ML libraries (Transformers, PyTorch, TensorFlow) Frameworks & Tools
- LangChain / LlamaIndex
- Vector DBs: Pinecone, FAISS, Weaviate, Chroma
- ML lifecycle tools: MLflow, Kubeflow GenAI Concepts
- Prompt Engineering
- RAG Architecture
- Fine-tuning & embeddings
- Model evaluation & monitoring Cloud & Deployment
- Experience with AWS / Azure / GCP
- Exposure to Azure OpenAI is a plus
- Containerization (Docker, Kubernetes) Architecture
- Experience designing scalable AI systems
- API development & microservices architecture Good to Have
- Experience with Agentic AI / Multi-Agent Systems
- Knowledge of MCP (Model Context Protocol)
- Experience with LLM orchestration frameworks
- Exposure to real-time AI applications
- Domain experience (Finance, Healthcare, Retail, etc.) Leadership & Soft Skills
- Strong problem-solving and analytical skills
- Experience leading AI/ML teams
- Excellent communication and stakeholder management
- Ability to translate business problems into AI solutions
Preferred Skills
- Stay updated with latest advancements in Generative AI Required Skills & Qualifications Core AI/ML
- Claude
- LangChain / LlamaIndex
- Vector DBs: Pinecone, FAISS, Weaviate, Chroma
- ML lifecycle tools: MLflow, Kubeflow GenAI Concepts
- Prompt Engineering
- RAG Architecture
- Fine-tuning & embeddings
- Model evaluation & monitoring Cloud & Deployment
- Experience with AWS / Azure / GCP
- Exposure to Azure OpenAI is a plus
- Containerization (Docker, Kubernetes) Architecture
- Experience designing scalable AI systems
- API development & microservices architecture Good to Have
- Experience with Agentic AI / Multi-Agent Systems
- Knowledge of MCP (Model Context Protocol)
- Experience with LLM orchestration frameworks
- Exposure to real-time AI applications
- Domain experience (Finance, Healthcare, Retail, etc.) Leadership & Soft Skills
- Experience leading AI/ML teams
- Ability to translate business problems into AI solutions
Job Title: Generative AI Lead / Gen AI Lead Location: Houston, Texas Fulltime Job Summary We are looking for an experienced Generative AI Lead to design, develop, and scale cutting-edge AI solutions using Large Language Models (LLMs) and modern AI frameworks. The ideal candidate will lead architecture, development, and deployment of GenAI applications, including agentic systems, conversational AI, and enterprise AI platforms. Key Responsibilities
Lead the design and architecture of Generative AI solutions using LLMs
Build and deploy LLM-powered applications (chatbots, copilots, RAG systems, summarization, etc.)
Develop agentic AI systems and multi-agent orchestration workflows
Work with prompt engineering, fine-tuning, and model optimization
Implement RAG (Retrieval-Augmented Generation) pipelines using vector databases
Integrate LLMs with enterprise systems via APIs
Ensure scalability, performance, and security of AI solutions
Collaborate with cross-functional teams (Data Engineering, DevOps, Product)
Mentor junior engineers and guide best practices in AI development
Stay updated with latest advancements in Generative AI Required Skills & Qualifications Core AI/ML
Strong experience in Generative AI & LLMs
Hands-on with models like:
OpenAI (GPT)
Claude
LLaMA / open-source LLMs Programming
Expertise in Python
Experience with AI/ML libraries (Transformers, PyTorch, TensorFlow) Frameworks & Tools
LangChain / LlamaIndex
Vector DBs: Pinecone, FAISS, Weaviate, Chroma
ML lifecycle tools: MLflow, Kubeflow GenAI Concepts
Prompt Engineering
RAG Architecture
Fine-tuning & embeddings
Model evaluation & monitoring Cloud & Deployment
Experience with AWS / Azure / GCP
Exposure to Azure OpenAI is a plus
Containerization (Docker, Kubernetes) Architecture
Experience designing scalable AI systems
API development & microservices architecture Good to Have
Experience with Agentic AI / Multi-Agent Systems
Knowledge of MCP (Model Context Protocol)
Experience with LLM orchestration frameworks
Exposure to real-time AI applications
Domain experience (Finance, Healthcare, Retail, etc.) Leadership & Soft Skills
Strong problem-solving and analytical skills
Experience leading AI/ML teams
Excellent communication and stakeholder management
Ability to translate business problems into AI solutions
Lead the design and architecture of Generative AI solutions using LLMs
Build and deploy LLM-powered applications (chatbots, copilots, RAG systems, summarization, etc.)
Develop agentic AI systems and multi-agent orchestration workflows
Work with prompt engineering, fine-tuning, and model optimization
Implement RAG (Retrieval-Augmented Generation) pipelines using vector databases
Integrate LLMs with enterprise systems via APIs
Ensure scalability, performance, and security of AI solutions
Collaborate with cross-functional teams (Data Engineering, DevOps, Product)
Mentor junior engineers and guide best practices in AI development
Stay updated with latest advancements in Generative AI Required Skills & Qualifications Core AI/ML
Strong experience in Generative AI & LLMs
Hands-on with models like:
OpenAI (GPT)
Claude
LLaMA / open-source LLMs Programming
Expertise in Python
Experience with AI/ML libraries (Transformers, PyTorch, TensorFlow) Frameworks & Tools
LangChain / LlamaIndex
Vector DBs: Pinecone, FAISS, Weaviate, Chroma
ML lifecycle tools: MLflow, Kubeflow GenAI Concepts
Prompt Engineering
RAG Architecture
Fine-tuning & embeddings
Model evaluation & monitoring Cloud & Deployment
Experience with AWS / Azure / GCP
Exposure to Azure OpenAI is a plus
Containerization (Docker, Kubernetes) Architecture
Experience designing scalable AI systems
API development & microservices architecture Good to Have
Experience with Agentic AI / Multi-Agent Systems
Knowledge of MCP (Model Context Protocol)
Experience with LLM orchestration frameworks
Exposure to real-time AI applications
Domain experience (Finance, Healthcare, Retail, etc.) Leadership & Soft Skills
Strong problem-solving and analytical skills
Experience leading AI/ML teams
Excellent communication and stakeholder management
Ability to translate business problems into AI solutions