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Job Description & How to Apply Below
AB InBev GCC was incorporated in 2014 as a strategic partner for Anheuser-Busch InBev. The center leverages the power of data and analytics to drive growth for critical business functions such as operations, finance, people, and technology. The teams are transforming Operations through Tech and Analytics.
Do You Dream Big?
We Need You.
Job Description
Job Title:
Data Scientist (Gen AI Experience)
Location:
Bengaluru
Reporting to:
Senior Manager - Analytics
1.Purpose of the role
The Global GenAI Team at Anheuser-Busch InBev (AB InBev) is tasked with constructing competitive solutions utilizing GenAI techniques. These solutions aim to extract contextual insights and meaningful information from our enterprise data assets. The derived data-driven insights play a pivotal role in empowering our business users to make well-informed decisions regarding their respective products.
In the role of a Data Scientist, you will operate at the intersection of:
LLM-based frameworks, tools, and technologies
Cloud-native technologies and solutions
Microservices-based software architecture and design patterns
As an additional responsibility, you will be involved in the complete development cycle of new product features, encompassing tasks such as the development and deployment of new models integrated into production systems. Furthermore, you will have the opportunity to critically assess and influence the product engineering, design, architecture, and technology stack across multiple products, extending beyond your immediate focus.
2.Key tasks & accountabilities
Large Language Models (LLM):
Experience with Lang Chain, Lang Graph
Proficiency in building agentic patterns like ReAct, ReWoo, LLMCompiler
Multi-modal Retrieval-Augmented Generation (RAG):
Expertise in multi-modal AI systems (text, images, audio, video)
Designing and optimizing chunking strategies and clustering for large data processing
Streaming & Real-time Processing:
Experience in audio/video streaming and real-time data pipelines
Low-latency inference and deployment architectures
NL2
SQL:
Natural language-driven SQL generation for databases
Experience with natural language interfaces to databases and query optimization
API Development:
Building scalable APIs with FastAPI for AI model serving
Containerization & Orchestration:
Proficient with Docker for containerized AI services
Experience with orchestration tools for deploying and managing services
Data Processing & Pipelines:
Experience with chunking strategies for efficient document processing
Building data pipelines to handle large-scale data for AI model training and inference
AI Frameworks & Tools:
Experience with AI/ML frameworks like Tensor Flow, Py Torch
Proficiency in Lang Chain, Lang Graph, and other LLM-related technologies
Prompt Engineering:
Expertise in advanced prompting techniques like Chain of Thought (CoT) prompting, LLM Judge, and self-reflection prompting
Experience with prompt compression and optimization using tools like LLMLingua, Ada Flow, Text Grad, and DSPy
Strong understanding of context window management and optimizing prompts for performance and efficiency
3.Qualifications, Experience, Skills
Level of educational attainment required
Bachelor's or Masterʼs degree in Computer Science, Engineering, or a related field.
Previous work experience required
Proven experience of 3+ years in developing and deploying applications utilizing Azure OpenAI and Redis as a vector database.
Technical skills required
Solid understanding of language model technologies, including Lang Chain, OpenAI Python SDK, Lamma Index, OLamma, etc.
Proficiency in implementing and optimizing machine learning models for natural language processing.
Experience with observability tools such as mlflow, langsmith, langfuse, weight and bias, etc.
Strong programming skills in languages such as Python and proficiency in relevant frameworks.
Familiarity with containerization and orchestration tools (e.g., Docker, Kubernetes).
And above all of this, an undying love for beer!
We dream big to create future with more cheers
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