Tiger Analytics - Senior Role - Data Engineering Artificial Intelligence

Who we are
Tiger Analytics is a global leader in AI and analytics, helping Fortune 1000 companies solve their toughest challenges. We offer full-stack AI and analytics services & solutions to empower businesses to achieve real outcomes and value at scale. We are on a mission to push the boundaries of what AI and analytics can do to help enterprises navigate uncertainty and move forward decisively. Our purpose is to provide certainty to shape a better tomorrow.
Our team of 4000+ technologists and consultants are based in the US, Canada, the UK, India, Singapore, and Australia, working closely with clients across CPG, Retail, Insurance, BFS, Manufacturing, Life Sciences, and Healthcare. We are a Great Place to Work-Certified- , recognized globally for our dedication to innovation and excellence.
Curious about the role?
We are seeking a highly experienced, deeply technical, and strategically minded AI Tech leader to bridge the gap between advanced Generative AI and enterprise-scale Data Engineering (DE). Aligned with various business units, you will serve as the premier technical leader driving the strategy, architecture, and end-to-end execution of complex, production-grade Agentic AI solutions.
This is a senior role requiring combination of solutioning capability and deep hands-on technical expertise. You will ensure that cutting-edge AI agents and LLM orchestration frameworks are seamlessly integrated with robust, scalable, and optimized big data pipelines.

What your typical day would look like?
As part of this role, you will:
- Architect End-to-End Agentic AI Systems: Lead the design, solutioning, and deployment of multi-agent LLM systems, RAG architectures, and autonomous workflows capable of solving complex enterprise challenges.
- Bridge AI & Data Engineering: Architect and optimize large-scale data lake environments, distributed computing frameworks, and high-throughput real-time streaming pipelines explicitly designed to feed and power GenAI applications.
- Drive Unit Alignment & Tech Solutioning: Partner closely with business unit leaders and executive stakeholders to translate vague business challenges into concrete, high-impact AI/DE roadmaps and technical solutions.
- Productionize & Scale MLOps/LLMOps: Establish and govern scalable MLOps and CI/CD pipelines to deploy, monitor, and continuously retrain agentic systems, effectively managing challenges like hallucination, bias, latency, and costs.
- Lead & Mentor Across Disciplines: Formally lead and mentor cross-functional technical teams comprising Data Engineers, AI Researchers, and Data Scientists, fostering technical excellence and cross-disciplinary innovation.
- Evaluate & Innovate: Continuously explore, prototype, and benchmark emerging open-source and proprietary tools within the evolving GenAI and Big Data landscapes to maintain a competitive technical edge.
Who do we expect ?
- 15+ years of overall technical experience in the Data Science, Analytics, and Data Engineering spaces.
- 5+ years of hands-on experience specifically within the Hadoop/Spark ecosystem or large-scale cloud data architecture.
- 3+ years of leadership experience managing, mentoring, and scaling high-performing, cross-functional technical teams.
- AI & Generative AI Technical Depth: Advanced expertise in LLMs, prompt engineering, RAG, and fine-tuning (LoRA, PEFT). Proven experience building and deploying autonomous LLM Agents using frameworks like LangChain, LlamaIndex, CrewAI, or AutoGen. Strong foundations in Deep Learning, ML, PyTorch, or TensorFlow.
- Data Engineering & Architecture Depth: Hands-on mastery of the Big Data ecosystem (HDFS, Hive, Kafka, Spark, Scala) , modern platforms (Databricks, Snowflake) , and Data Lake design. Proficient in NoSQL/Vector databases (Cassandra, Pinecone, Milvus) and optimizing distributed computing for massive AI workloads.
- Cloud, Infrastructure & MLOps: Proven experience architecting large-scale cloud solutions (AWS, Azure, or GCP) with robust MLOps/DataOps pipelines, containerization (Docker, Kubernetes) , and microservices-driven backend APIs (FastAPI/Django).