Senior Applied AI Engineer

Givzey

1d•Remote

About The Position

Givzey | Version2.ai is hiring a Senior Applied AI Engineer to lead the technical design and delivery of high-impact AI initiatives across its products. This is a senior individual-contributor and technical-leadership role for an experienced software engineer with deep expertise in building production AI systems. The engineer will lead small teams delivering capabilities such as agentic workflows, retrieval systems, autonomous business processes, and intelligent product experiences. The role involves taking ownership of complex initiatives, translating ambiguous problems into actionable engineering work, making architectural decisions, and guiding delivery from concept through production. The engineer will remain hands-on in implementation while raising engineering standards through mentorship, code reviews, architectural guidance, and collaboration. The work spans production AI systems, backend services and integrations, evaluation and observability, and infrastructure for scalable AI applications. Givzey | Version2.ai is a fully remote, AI-native company focused on autonomous fundraising. Its Virtual Engagement Officers manage donor portfolios and deliver personalized outreach for nonprofit organizations.

Requirements

Must be a US citizen or authorized to work in the United States.
7+ years of professional software engineering experience, including substantial experience designing and delivering production systems.
Strong proficiency in Python and modern backend software engineering.
Demonstrated technical leadership on engineering projects or small teams, including architecture, implementation, mentorship, and delivery.
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 RAG architectures.
Strong understanding of production AI challenges, including model reliability, hallucination mitigation, evaluation, observability, latency, and cost management.
Commitment to software engineering best practices, including automated testing, code review, CI/CD, system design, and maintainability.
Experience working with cloud infrastructure, preferably AWS.
Ability to mentor engineers, provide constructive technical feedback, and influence engineering practices.
Strong technical judgment and debugging skills, with the ability to navigate complex systems and ambiguous requirements.
Ability to balance hands-on engineering with technical leadership responsibilities.
Excellent communication and collaboration skills across engineering, product, and business teams.
Experience in fast-moving environments where ownership, adaptability, and execution are essential.

Nice To Haves

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 technical maturity.
Experience balancing technical debt, delivery speed, operational reliability, and long-term architectural decisions.

Responsibilities

Lead small teams of engineers in designing, developing, and delivering complex, high-impact AI initiatives.
Own technical execution from initial design through implementation, testing, deployment, and ongoing operation.
Translate ambiguous product requirements into technical approaches, actionable engineering tasks, and delivery milestones.
Drive architectural discussions, evaluate tradeoffs, and establish implementation strategies.
Coordinate engineering efforts, identify dependencies and blockers, and maintain accountability for delivery.
Contribute production code and solve challenging technical problems alongside the team.
Collaborate with Product, Engineering leadership, and stakeholders to deliver meaningful business outcomes.
Design, build, and maintain production-grade AI systems using LLMs, agentic workflows, retrieval architectures, and backend services.
Develop agent workflows using tool calling, structured outputs, multi-step reasoning, deterministic validation, and human-in-the-loop controls.
Build and optimize RAG systems, including ingestion pipelines, embeddings, semantic retrieval, and context assembly.
Integrate foundation models through platforms such as Amazon Bedrock and AWS AgentCore.
Develop evaluation frameworks for AI workflows, including regression testing, golden datasets, trace analysis, and quality measurement.
Improve AI system reliability, accuracy, latency, observability, and cost efficiency, while addressing unpredictable model behavior and production constraints.
Elevate engineering standards through code reviews, architectural guidance, mentorship, and technical leadership.
Mentor engineers in software design, debugging, testing, system architecture, and production engineering.
Establish and reinforce practices for code quality, automated testing, documentation, maintainability, and operational readiness.
Identify technical weaknesses, unnecessary complexity, and process inefficiencies, and help address them.
Promote reliable, understandable, testable, and maintainable engineering solutions.