AI Engagement Lead
New
T
Tiger AnalyticsAI Analytics Consulting
San Francisco, California, United StatesFull-TimeLead
Salary not disclosed
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Job Details
- Experience
- 10+ years
- Required Skills
- DockerPythonCloud ComputingMachine LearningRESTful APIsGenerative AILangChain
Requirements
- 10+ years of experience in software engineering, AI/ML engineering, data science, or a related technical field.
- Strong hands-on experience building and deploying AI/ML or Generative AI solutions.
- Proven experience leading technical teams or AI engineering pods while remaining hands-on.
- Strong proficiency in Python and experience developing production-grade applications.
- Strong understanding of LLMs, Generative AI, NLP, RAG, and AI agents.
- Experience with one or more AI/GenAI frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, or equivalent.
- Experience working with LLM APIs/foundation models such as OpenAI, Azure OpenAI, Anthropic, Bedrock, Gemini, or open-source LLMs.
- Experience with vector databases and semantic search.
- Experience designing and deploying cloud-based AI solutions on AWS, Azure, or GCP.
- Strong understanding of APIs, microservices, Docker, CI/CD, and production deployment.
- Strong client-facing communication and stakeholder management skills.
- Master's in Business Analytics or equivalent work experience.
Responsibilities
- Lead AI/GenAI engagements from discovery and solution definition through development, deployment, and production.
- Serve as the primary technical and delivery interface for clients and senior stakeholders.
- Understand business objectives and translate them into AI/ML solution requirements and actionable engineering plans.
- Own project planning, prioritization, timelines, milestones, risks, dependencies, and overall delivery.
- Coordinate across AI Engineers, Data Scientists, Data Engineers, Product Managers, and client teams.
- Conduct regular client discussions, status reviews, technical walkthroughs, and solutioning sessions.
- Architect, develop, and deploy AI/ML and Generative AI solutions for enterprise use cases.
- Lead hands-on development of LLM-powered applications, RAG systems, AI agents, and agentic workflows.
- Design and implement end-to-end AI application architectures including LLM integration, prompt engineering, RAG pipelines, and vector databases.
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