Jenosize Digital Group
Linkedin · Posted 13d ago
Lead AI Engineer
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Indexed description
Key Responsibilities
- 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
- 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.
- 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.
- 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.
- 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.
- 3+ 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
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