Pay Range: $55hr - $60hr
Responsible for designing, developing, and deploying production-grade AI applications and agentic systems powered by large language models.
The role focuses on building scalable AI solutions, implementing retrieval-augmented generation pipelines, integrating intelligent systems with enterprise platforms, and driving reliable and high-performing AI architectures.
Responsibilities:
Architect and deliver end-to-end LLM-powered applications and agentic workflows using Python.
Design and implement RAG pipelines using enterprise data, embeddings, and vector databases.
Build multi-step, tool-using AI agents with planning, execution, and memory capabilities.
Utilize frameworks such as LangChain, LangGraph, and AutoGen for agent orchestration.
Integrate AI systems with APIs, backend services, and cloud platforms.
Establish evaluation, reliability, scalability, and performance strategies for AI solutions.
Optimize AI systems for accuracy, latency, and operational efficiency.
Design scalable cloud-based architectures for AI applications.
Collaborate with cross-functional teams to deliver intelligent enterprise solutions.
Support deployment, monitoring, troubleshooting, and enhancement of AI-powered systems.
Requirement/Must Have:
Strong expertise in Python development.
Experience building and deploying production-grade backend systems.
Hands-on experience developing applications using large language models.
Experience with prompt engineering and orchestration techniques.
Proven experience with RAG architectures, embeddings, and vector databases.
Experience with agentic frameworks such as LangChain, LangGraph, or AutoGen.
Strong system design and scalable application architecture skills.
Experience building and scaling cloud-based applications.
Strong analytical and problem-solving skills.
Excellent communication and collaboration abilities.
Should Have:
Experience integrating AI systems with enterprise APIs and backend services.
Knowledge of AI evaluation and monitoring strategies.
Experience optimizing AI systems for performance, reliability, and cost efficiency.
Familiarity with enterprise AI deployment and operational best practices.
Skills:
Python.
Large Language Models.
Prompt engineering.
RAG architectures.
Embeddings and vector databases.
LangChain.
LangGraph.
AutoGen.
Agentic AI systems.
API integration.
Cloud application development.
Backend system development.
AI orchestration.
System design and scalability.
Performance optimization.
Qualification And Education:
Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or related field preferred.
Advanced certifications or experience in AI/ML technologies preferred.