Role: Lead Agentic AI engineer
Location: Dallas, TX, Atlanta, GA, Boston, MA, Chicago, IL (Hybrid)
Duration: W2 Contract (Independent candidates on W2)
Ready to apply Before you do, make sure to read all the details pertaining to this job in the description below.
This position will design and build intelligent AI agent systems, LLM-based applications, and autonomous workflow solutions within the context of our enterprise applications. We are looking for a candidate to provide expertise in Large Language Models, multi-agent architectures, RAG systems, Python development, cloud AI services, and enterprise AI integration. This individual will have broad experience in developing and deploying agentic AI solutions that can autonomously perform complex business tasks.
Experience & Skills
4-6 years’ hands-on experience working as Sr AI/ML Developer with at least three complete agentic AI system implementations
4-6 years’ hands-on experience with cloud AI services (Azure OpenAI, AWS Bedrock, Google Vertex AI)
4-6 years hands-on experience with Python AI/ML frameworks (LangChain, LlamaIndex, Transformers, PyTorch)
4-6 years’ hands-on experience integrating LLMs with external systems and enterprise applications
Experience working with vector databases, knowledge graphs, and RAG pipeline development
Advising on best practices for AI agent development and enterprise AI integration processes
Experience in deploying AI models and agents in multiple environments (dev, staging, production)
Experience managing stakeholder communication regarding AI capabilities, limitations, and project timelines, including managing expectations, foreseeing AI-related risks and reporting them
Technical Skills & Competencies
Python, FastAPI, Flask
LangChain, LlamaIndex, Transformers library
OpenAI API, Azure OpenAI, Anthropic Claude
Vector databases (Pinecone, Weaviate, ChromaDB)
Git, Docker, Kubernetes
JavaScript/TypeScript for AI integration
Postman for API testing
SQL/NoSQL databases for AI data management
Sound knowledge in cloud AI services and MLOps
AI model performance monitoring and optimization
Prompt engineering and AI safety practices
Responsibilities
Proficient in developing, deploying, and orchestrating multi-agent AI systems
Demonstrated proficiency in LLM fine-tuning, prompt engineering, and model optimization
Demonstrated proficiency in designing and implementing RAG (Retrieval-Augmented Generation) systems
Demonstrated proficiency in understanding and implementing autonomous business workflows and AI-driven processes
Demonstrated proficiency in using AI frameworks like LangChain, LlamaIndex, and Hugging Face ecosystem
Demonstrated proficiency with Python development xsgimln and AI/ML libraries
Demonstrated proficiency in JavaScript/TypeScript for AI frontend integration
Ability to perform AI model performance tuning and optimization in production environments
Familiarity with MLOps tools and practices
Experience in building conversational AI interfaces, chatbots, and AI-powered applications
Experience with API development for AI services and webhook management
Experience with cloud AI platforms (Azure OpenAI, AWS Bedrock, Google Cloud AI)
Experience in AI agent orchestration, planning algorithms, and decision-making frameworks
Creating and maintaining vector databases and knowledge management systems
Experience producing AI system architecture and technical design documentation
Must have college degree in Computer Science, AI/ML, or equivalent experience