Hands-on engineering role focused on designing and building generative AI solutions
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
Requirements
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)
Strong Plus
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