Job description

Tiger Analytics is seeking a highly experienced Lead AI Engineer to lead the end-to-end AI Engineering workstream for the Luma platform. This is a hands-on technical leadership role responsible for driving the architecture, design, and delivery of enterprise-scale Agentic AI solutions while serving as the primary technical interface for the client. The ideal candidate will combine deep expertise in AI engineering, distributed systems, cloud-native architectures, and MLOps with exceptional stakeholder management skills. This individual will oversee multiple engineering pods, mentor technical teams, drive engineering excellence, and partner closely with client leadership to shape the AI roadmap and ensure successful delivery. Requirements Key Responsibilities • Lead AI architecture and engineering roadmap for Agentic AI platforms. • Design and deliver scalable, production-grade AI solutions. • Oversee AI platform services, backend APIs, microservices, MLOps, observability, and cloud infrastructure. • Partner with clients to translate business needs into AI solutions and lead architecture discussions. • Mentor AI engineers, conduct design/code reviews, and drive engineering best practices. • Lead multiple engineering teams, manage delivery, risks, and technical roadmaps. • Evaluate and adopt emerging AI technologies and frameworks. Required Skills • AI/ML: GenAI, LLMs, Agentic AI, Multi-Agent Systems, RAG, Prompt Engineering, AI Guardrails, MLOps. • Programming: Python (expert), Golang, REST APIs, Async Programming. • Cloud: AWS (Bedrock/AgentCore preferred), Lambda, ECS/EKS, API Gateway, Step Functions, S3, DynamoDB, CloudWatch. • Backend: Microservices, Docker, Kubernetes, Distributed Systems. • Leadership: Client-facing consulting, architecture design, stakeholder management, mentoring, and technical leadership. Qualifications • 10–15+ years in software engineering with 5+ years leading AI/ML engineering teams . • Experience building and deploying enterprise AI platforms on AWS. • Strong client-facing, solution architecture, and cross-functional leadership experience. Preferred • AWS Bedrock, LangGraph, CrewAI, AutoGen, AI Copilots, AI Observability, Enterprise SaaS, and large-scale distributed systems. Benefits Significant career development opportunities exist as the company grows. The position offers a unique opportunity to be part of a small, fast-growing, challenging and entrepreneurial environment, with a high degree of individual responsibility. Tiger Analytics provides equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, pregnancy, national origin, ancestry, marital status, protected veteran status, disability status, or any other basis as protected by federal, state, or local law.

Requirements

Must-haves

Lead AI architecture and engineering roadmap for Agentic AI platforms.
Design and deliver scalable, production-grade AI solutions.
Oversee AI platform services, backend APIs, microservices, MLOps, observability, and cloud infrastructure.
Partner with clients to translate business needs into AI solutions and lead architecture discussions.
Mentor AI engineers, conduct design/code reviews, and drive engineering best practices.
Lead multiple engineering teams, manage delivery, risks, and technical roadmaps.
Evaluate and adopt emerging AI technologies and frameworks.
AI/ML: GenAI, LLMs, Agentic AI, Multi-Agent Systems, RAG, Prompt Engineering, AI Guardrails, MLOps.
Programming: Python (expert), Golang, REST APIs, Async Programming.
Backend: Microservices, Docker, Kubernetes, Distributed Systems.
Leadership: Client-facing consulting, architecture design, stakeholder management, mentoring, and technical leadership.
10–15+ years in software engineering with 5+ years leading AI/ML engineering teams .
Experience building and deploying enterprise AI platforms on AWS.
Strong client-facing, solution architecture, and cross-functional leadership experience.

Nice-to-haves

Cloud: AWS (Bedrock/AgentCore preferred), Lambda, ECS/EKS, API Gateway, Step Functions, S3, DynamoDB, CloudWatch.
AWS Bedrock, LangGraph, CrewAI, AutoGen, AI Copilots, AI Observability, Enterprise SaaS, and large-scale distributed systems.

Application prep

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Keywords to mirror

aimlpythongolangrestawsapidockerkubernetes

Platform: workable
Effort: medium