Zealant Consulting Group

About the Role
We are reimagining software development for the AI era. Traditional development models involving separate product managers, designers, and engineering teams are evolving toward a model where engineers own product outcomes end-to-end.
As an SDE 3 – AI Native Development, you will operate as a Team of One, leveraging AI tools, automation, and modern engineering infrastructure to transform ideas into production-ready products. You will own the entire product lifecycle—from ideation and prototyping to deployment, optimization, and scaling.
This role is ideal for engineers who think like product owners, move quickly from concept to execution, and build reliable, scalable systems rather than simply implementing predefined tickets.
Key Responsibilities
1. End-to-End Product Ownership
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Translate business objectives into functional, deployable software systems.
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Define system architecture, workflows, and implementation strategies.
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Own products throughout the lifecycle, from ideation to production and scale.
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Build, deploy, and maintain production-grade systems rather than standalone prototypes.
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2. Rapid Prototyping and Iteration
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Convert ambiguous ideas into working prototypes within hours or days.
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Leverage coding, AI tools, automation, and low-code platforms to accelerate development.
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Validate solutions through experiments, user feedback, and measurable outcomes.
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Iterate rapidly to transform prototypes into reliable, production-ready products.
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3. AI-Native System Development
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Design and orchestrate LLM agents, retrieval pipelines, and AI-driven workflows.
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Build memory layers, API integrations, automation systems, and appropriate guardrails.
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Develop resilient AI applications that account for hallucinations and model variability.
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Implement mechanisms to identify and mitigate quality degradation over time.
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4. Scalability, Cost, and Performance Optimization
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Evaluate token consumption, inference costs, latency, and infrastructure requirements.
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Design systems that balance performance, reliability, scalability, and cost efficiency.
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Identify architectural trade-offs and optimize systems for production workloads.
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Build resilient solutions capable of operating efficiently at scale.
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5. Operational Excellence
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Implement monitoring, telemetry, and observability across deployed systems.
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Establish evaluation frameworks and feedback loops for continuous quality improvement.
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Automate repetitive workflows to improve engineering productivity and delivery speed.
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Maintain clear, structured, and repeatable technical documentation.
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Minimum Qualifications
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Demonstrated experience building and shipping production-grade software systems.
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Strong systems thinking, analytical reasoning, and problem-solving abilities.
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High ownership mindset with the ability to work independently and make decisions.
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Comfort working with ambiguity, rapid experimentation, and iterative development.
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Experience with APIs, automation tools, or AI development platforms.
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Ability to evaluate infrastructure trade-offs, system reliability, and performance requirements.
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Proficiency in at least one modern programming language or application development ecosystem.
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Preferred Qualifications
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Experience building LLM-powered applications or AI-driven products.
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Familiarity with AI evaluation frameworks, monitoring, and instrumentation.
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Experience working with data pipelines, analytics, or telemetry systems.
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Strong user experience (UX) awareness and front-end development sensibility.
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A portfolio, GitHub profile, or demonstrable examples of deployed production systems.
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Technology Stack
Experience with one or more of the following technologies:
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Front-End: React, Vue.js, Angular
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Back-End: Node.js, Python, Java
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AI &amp; Automation: LLMs, AI agents, retrieval pipelines, workflow automation, and API integrations
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What Success Looks Like
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Reduces the time required to move ideas from concept to production.
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Consistently delivers reliable, production-grade systems rather than isolated experiments.
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Makes sound engineering decisions balancing quality, latency, scalability, and cost.
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Builds measurable evaluation loops that continuously improve product performance.
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Demonstrates end-to-end ownership of product outcomes with minimal supervision.
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Ideal Candidate
A product-minded engineer who combines strong software engineering fundamentals with AI-native development practices, rapid execution, and the ability to independently take ideas from concept to production.