AI Engagement Lead

Tiger Analytics is an advanced analytics consulting firm. We are the trusted analytics partner for several Fortune 100 companies, enabling them to generate business value from data. Our consultants bring deep expertise in Data Science, Machine Learning, and AI. Our business value and leadership have been recognized by various market research firms, including Forrester and Gartner.

We are looking for an AI Engagement Lead / AI Engineering Pod Lead who can combine strong client and project leadership with hands-on expertise in AI/ML and Generative AI engineering. The role will involve approximately 50% engagement/project management and coordination and 50% hands-on technical leadership and AI engineering.

• →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.
• →Proactively identify delivery risks, technical challenges, resource constraints, and dependencies and drive them toward resolution.
• →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, Embeddings and vector databases, Tool/function calling, Agent orchestration, Evaluation and monitoring
• →Work with frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, or equivalent technologies.
• →Integrate foundation models and LLM platforms such as OpenAI, Azure OpenAI, Anthropic Claude, Amazon Bedrock, Gemini, or open-source models.
• →Develop production-grade AI services and APIs using technologies such as Python, FastAPI, Docker, Kubernetes, and cloud platforms.
• →Design retrieval pipelines including document processing, chunking, embedding generation, vector search, hybrid retrieval, and re-ranking.

• 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.
• Experience with AI evaluation, monitoring, guardrails, and responsible AI is highly desirable.
• Strong client-facing communication and stakeholder management skills.
• Demonstrated ability to translate ambiguous business problems into practical technical solutions.
• Master's in Business Analytics or equivalent work experience.