Generative AI Engineer, Archer

• 7–8 years of full-stack engineering experience building scalable, cloud-native applications
• 2-4+ years of hands-on experience designing and deploying Generative AI applications or LLM-powered systems
• Practical experience with OpenAI GPT, Azure OpenAI, AWS Bedrock, Gemini, Vertex AI, or similar LLM ecosystems
• Strong background designing and optimizing RAG architectures and retrieval workflows
• Experience with at least one orchestration or agent framework, such as:
• LangChain
• LangGraph
• LlamaIndex
• Haystack
• Semantic Kernel
• DSPy
• Or custom orchestration pipelines
• Experience with vector databases, such as Pinecone, Weaviate, FAISS, Milvus, or similar technologies
• Experience implementing and evaluating embedding models and retrieval pipelines (dense, hybrid, cross-encoder)
• Proficiency with prompt engineering, including chain prompting, iterative refinement, and structured output patterns
• Strong frontend development, and backend experience with Node.js/Python, REST APIs, SQL/NoSQL databases
• Deployment experience with Docker, Kubernetes, and CI/CD pipelines across AWS, Azure, or GCP
• A hands-on engineer who enjoys solving complex problems and delivering production-ready features
• Able to design architectures and write clean, maintainable code end-to-end
• Curious and constantly learning new AI techniques and frameworks
• A strong communicator who can articulate technical concepts clearly to both technical and business teams
• Comfortable operating in fast-paced environments with evolving tools, models, and expectations

Desirable

• We are looking for a hands-on Generative AI Engineer to join our Corporate Technology group to design, build, and deploy AI-driven applications powered by Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and agent-based systems
• You will create scalable, production-ready AI solutions that enhance natural language understanding and seamlessly integrate intelligence into our product ecosystem
• This role blends full-stack engineering, LLM architecture, and AI system integration—ideal for a mid-level engineer who can both design and implement features end-to-end
• You will collaborate closely with product, engineering, and business teams to align technical design with real-world objectives while continually learning and evolving with the GenAI landscape
• Develop, deploy, and maintain LLM-powered applications supporting natural language understanding, conversational search, and intelligent automation
• Build and optimize RAG pipelines, including embeddings, chunking strategies, retrieval logic, ranking, evaluation, and continuous improvement loops
• Design and implement agent-based workflows for reasoning, tool usage, routing, and structured task execution
• Apply advanced prompt engineering techniques—including chain prompting, few-shot prompting, and structured prompting—to improve LLM reliability and output quality
• Architect and implement high-performance applications and scalable backend systems that incorporate AI capabilities into diverse solutions
• Build full-stack features using React (or similar), Node.js/Python, REST/GraphQL APIs, and modern backend patterns
• Deploy and operate AI applications in cloud environments (AWS, Azure, GCP) using Docker, Kubernetes, and CI/CD tooling
• Ensure system-level reliability with robust monitoring, observability, telemetry, and performance metrics for LLM workloads
• Partner with product managers, business teams, and engineers to align AI initiatives with business priorities and user needs
• Translate technical concepts through clear presentations, design reviews, and documentation, ensuring shared understanding across teams
• Participate in scoping, requirements definition, and architectural discussions, influencing design decisions and system roadmaps
• Implement evaluation frameworks for RAG, agents, and LLM responses (latency, retrieval accuracy, hallucination detection, etc.)
• Continuously refine data quality, retrieval accuracy, indexing strategies, and model selection processes
• Drive process improvements across the AI development lifecycle to enhance system robustness and engineering productivity
• Diagnose and resolve issues across retrieval layers, LLM integration, application logic, and infrastructure
• Please note that this job description is intended to provide a general overview of the position and does not include an exhaustive list of responsibilities and qualifications