[Hiring] Senior Applied AI Engineer @Givzey

Role Description

We're hiring a Senior Applied AI Engineer to lead the technical design and delivery of high-impact AI initiatives across our products. This is a senior individual contributor and technical leadership role for an experienced software engineer with deep expertise in building production AI systems. You'll lead small teams of engineers responsible for delivering sophisticated AI capabilities, from agentic workflows and retrieval systems to autonomous business processes and intelligent product experiences.

We're looking for someone who brings more than technical expertise. You should be capable of:

• Taking ownership of complex initiatives
• Breaking ambiguous problems into actionable engineering work
• Making sound architectural decisions
• Guiding a team from concept through production delivery

Equally important, you'll play a significant role in raising the technical maturity of our engineering organization through:

• Mentorship
• Code reviews
• Architectural guidance
• Hands-on collaboration

We value engineers who combine deep technical ability with practical judgment, strong ownership, and a genuine investment in making the people around them better.

Qualifications

• 7+ years of professional software engineering experience, with substantial experience designing and delivering production systems.
• Strong proficiency in Python and modern backend software engineering.
• Demonstrated experience providing technical leadership for engineering projects or small teams, including architecture, implementation, mentorship, and delivery.
• Proven hands-on experience designing, building, and shipping production AI applications using LLMs, agentic workflows, retrieval systems, or related technologies.
• Strong knowledge of backend architecture, APIs, distributed systems, databases, and workflow orchestration.
• Experience designing agentic workflows, tool-calling systems, structured outputs, or retrieval-augmented generation architectures.
• Strong understanding of production AI challenges, including model reliability, hallucination mitigation, evaluation, observability, latency, and cost management.
• Demonstrated commitment to software engineering best practices, including automated testing, code review, CI/CD, system design, and maintainability.
• Experience working with cloud infrastructure, preferably AWS.
• Proven ability to mentor engineers, provide constructive technical feedback, and influence engineering practices.
• Strong technical judgment, debugging skills, and the ability to navigate complex systems and ambiguous requirements.
• Ability to balance hands-on engineering contributions with technical leadership responsibilities.
• Excellent communication and collaboration skills, particularly when working across engineering, product, and business teams.
• Experience operating in fast-moving environments where ownership, adaptability, and execution are essential.

Requirements

• Experience with Amazon Bedrock, AWS AgentCore, Lambda, Step Functions, S3, DynamoDB, RDS, SQS, or related AWS services.
• Experience with LangGraph, LangChain, DSPy, Semantic Kernel, or similar agent orchestration frameworks.
• Experience building multi-step autonomous agents that interact with tools, APIs, external systems, or business workflows.
• Experience developing AI evaluation frameworks, prompt regression testing, trace analysis, or human-in-the-loop review systems.
• Experience with vector databases and semantic retrieval technologies such as OpenSearch, pgvector, Pinecone, Weaviate, or FAISS.
• Experience with LLM observability and evaluation platforms such as LangSmith, Arize, Helicone, Weights & Biases, or OpenTelemetry.
• Experience leading technical initiatives within startup or rapidly scaling SaaS organizations.
• Experience improving engineering practices, introducing development standards, or helping teams increase their technical maturity.
• Experience balancing technical debt, delivery speed, operational reliability, and long-term architectural decisions.

Benefits

• High-impact AI initiatives are delivered successfully through effective technical leadership, clear ownership, and strong collaboration.
• AI systems are reliable, observable, maintainable, and trusted by customers and internal teams.
• Engineers working alongside you demonstrate measurable growth in technical ability, independence, and engineering judgment.
• Code quality, testing practices, architectural consistency, and operational reliability improve across the teams you lead.
• Complex technical initiatives move from ambiguous requirements to production-ready solutions with greater consistency and predictability.
• Engineering decisions balance innovation, delivery speed, maintainability, and long-term business value.
• Your technical leadership enables the broader organization to deliver increasingly sophisticated AI capabilities with confidence.