Background

Jenosize is building AI Employee and enterprise AI systems that do more than answer chat messages. The next generation of our AI products must coordinate work across teams, tools, data sources, and specialized agents. We need a Lead AI Engineer who can turn agentic AI from a demo into a reliable production system that improves real business operations. This role sits at the intersection of software engineering, AI architecture, product thinking, and team orchestration. The person must be senior enough to design the system, lead engineers, make technical trade-offs, define quality gates, and collaborate with Product, Business, HR, and Client teams. The strongest candidate is not only good at prompting or integrating LLM APIs. They understand distributed systems, tool execution, memory design, evaluation, observability, security, cost control, and how humans should supervise AI work. They can build deep-agent style workflows where one AI can decompose goals, delegate subtasks, coordinate specialist agents, verify outputs, and recover from failure. Role Purpose

Role Purpose

The Lead AI Engineer owns the architecture and execution of Jenosize's agentic AI platform layer. The mission is to design and ship reliable AI team-work orchestration systems that help humans and AI agents work together across real company workflows, with measurable improvements in speed, quality, and operational leverage

Key Responsibilities

1. Agentic AI Architecture

Design multi-agent orchestration flows for complex business workflows such as HR operations, sales support, proposal production, customer support, and executive operations.
Build planner / deep-agent style systems that can break down goals, assign subtasks, use tools, verify outputs, and escalate to humans when needed.
Define agent contracts, tool interfaces, memory strategy, state management, routing, retries, fallback behavior, and human-in-the-loop gates.
Translate product goals into maintainable AI system architecture, not one-off demos

2. Engineering Leadership & Delivery

Lead engineering execution from prototype to production deployment.
Review architecture, code quality, reliability, security, and scalability of AI systems.
Make build-vs-buy decisions for frameworks, model providers, vector stores, orchestration layers, evaluation tools, and observability stacks.
Mentor engineers on agentic system design, testing, prompt/tool design, and production AI best practices.

3. Evaluation, Reliability & Observability

Define measurable quality metrics for AI workflows: task success rate, tool-call accuracy, hallucination rate, escalation rate, latency, cost per task, and user satisfaction.
Build evaluation harnesses for agent workflows, including test cases, regression checks, synthetic tasks, and real-work QA loops.
Implement logs, traces, state inspection, error classification, cost monitoring, and alerting for AI operations.
Design guardrails for privacy, access control, prompt injection, unsafe tool calls, and sensitive company data.

4. Cross-Functional AI Teamwork

Work with Product, Design, HR, Sales, CS, and Management to map business workflows into agentic execution plans.
Communicate technical trade-offs clearly to both executives and engineers.
Help teams understand where AI should act autonomously, where it should assist, and where humans must stay in control.
Create reusable playbooks and patterns so future AI teams can build faster.

5. Business Impact & Platform Thinking

Tie engineering work to measurable outcomes such as reduced operation time, faster proposal cycles, higher HR response quality, lower support workload, or improved product retention.
Design systems that can scale across multiple clients, departments, and use cases without rebuilding from scratch.
Manage cost, latency, model selection, and operational risk as first-class product constraints. -
Build platform components that become Jenosize's long-term competitive advantage.

Qualifications

5+ years of software engineering experience, with at least 2 years in AI/ML, LLM application development, automation platforms, or distributed systems.
Strong backend engineering skills in TypeScript/Node.js, Python, or equivalent production stack.
Hands-on experience building LLM applications with tool use, retrieval, function calling, workflow orchestration, or agent frameworks.
Strong understanding of API design, queues/background jobs, database design, auth/access control, observability, and cloud deployment.
Can design evaluation and monitoring for AI systems, not just rely on manual testing.
Able to lead engineers, review architecture, and make decisions under ambiguity.
Comfortable communicating with business leaders and non-technical stakeholders

How to Apply:

Send your resume and portfolio (if applicable) to hr@jenosize.com. Share your experience with Generative AI and how you can contribute to building next-gen AI solutions at Jenosize.