Generative AI Engineer

Owns technical implementation of AI initiatives - from prototype * production deployment

Works closely with AI Product Delivery Leads, business stakeholders, and engineering teams

Deep, day-to-day development using LLMs, APIs, and AI frameworks

Duties:

• Design and implement generative AI solutions (RAG, automation, agentic workflows)
• Translate product requirements and use cases into scalable technical architectures
• Build and optimize prompt strategies, LLM workflows, and agent-based systems
• Develop Retrieval-Augmented Generation (RAG) pipelines using enterprise data
• Integrate LLMs into applications via APIs and microservices
• Collaborate with product and delivery leads to iterate on features and solutions
• Evaluate and improve model performance (accuracy, latency, cost, reliability)
• Implement logging, monitoring, and guardrails for AI systems
• Rapidly prototype and test new AI capabilities
• Leverage AI tools to accelerate development, debugging, and experimentation

Do you have experience in Systems integration?, * 3-6+ years of software engineering experience

• Strong programming skills (Python preferred)
• Hands-on experience working with LLMs (e.g., OpenAI, Claude, open-source models)
• Experience building APIs and working with backend systems
• Familiarity with data pipelines, system integration, and cloud environments
• Understanding of how AI/ML systems work (not just usage, but implementation)

Nice to Have

• Experience with RAG architectures and vector databases
• Familiarity with frameworks (LangChain, LlamaIndex, etc.)
• Experience with agentic workflows or tool-using LLM systems
• Exposure to fine-tuning or deploying open-source models
• Experience in enterprise-scale environments, * Python backend development : 4 years (Required)
• RAG systems: 3 years (Required)
• building and deploying production LLM/GenAI systems: 3 years (Required)

From $65 an hour - Contract