AI Engineer

Dreaming big is in our DNA. It’s who we are as a company. It’s our culture. It’s our heritage. And more than ever, it’s our future. A future where we’re always looking forward. Always serving up new ways to meet life’s moments. A future where we keep dreaming bigger. We look for people with passion, talent, and curiosity, and provide them with the teammates, resources and opportunities to unleash their full potential. The power we create together – when we combine your strengths with ours – is unstoppable. Are you ready to join a team that dreams as big as you do?

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 Title: AI Engineer

Location: Bangalore (Onsite)

Reporting to: Senior Manager - AI Engineering

1. PURPOSE OF ROLE :

The AI Engineer will be a hands-on individual contributor responsible for building, reviewing, and delivering complex GenAI solutions for Insights Copilot and related enterprise AI products. The role requires strong engineering ownership, the ability to use AI tooling efficiently for rapid prototyping and development, and close collaboration with principal engineers to convert ambiguous business problems into reliable, scalable, production-ready solutions.

2. KEY TASKS AND ACCOUNTABILITIES :

• Develop and deliver AI and GenAI solutions leveraging LLMs, RAG pipelines, agentic workflows, and enterprise integrations.

• Utilize AI-assisted development tools to accelerate prototyping, development, testing, debugging, and documentation while maintaining engineering quality.

• Contribute to code reviews and adhere to best practices for code quality, security, performance, and maintainability.

• Collaborate with senior engineers and technical leads to implement solution designs and resolve technical challenges.

• Own assigned features and modules throughout the development lifecycle, including testing, deployment, and production support.

• Build and enhance LLM-powered applications using LangChain, LangGraph, and agentic frameworks.

• Design and optimize RAG solutions, including chunking, retrieval, embeddings, and context management strategies.

• Develop APIs using FastAPI and integrate AI services with databases, enterprise systems, and cloud platforms.

• Implement scalable AI workflows using orchestration, caching, and asynchronous processing techniques.

• Apply prompt engineering, evaluation frameworks, and testing methodologies to improve AI solution quality.

• Ensure LLM-based solutions incorporate grounding, evaluation, traceability, and guardrails to support reliable analytical outcomes.

• Analyze business and technical requirements to recommend and implement appropriate AI/ML approaches.

• Monitor, troubleshoot, and optimize AI applications for performance, scalability, reliability, and latency.

• Evaluate solution effectiveness through experimentation, metrics, and continuous improvement practices.

• Apply machine learning fundamentals to support predictive, classification, recommendation, NLP, and GenAI use cases.

• Collaborate with product, data, platform, and business teams, as well as data scientists and engineers, to deliver end-to-end AI solutions.

• Contribute to documentation, reusable assets, and engineering best practices that improve team productivity and solution quality.

3. QUALIFICATIONS, EXPERIENCE, SKILLS

Education

• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, Artificial Intelligence, Machine Learning, or a related field; equivalent practical experience will also be considered.

Experience

• Minimum 3 to 5 years of experience in software engineering, AI engineering, machine learning engineering, data engineering, or a related technical field.

• Hands-on experience building and supporting AI applications, LLM-based solutions, RAG workflows, APIs, or machine learning systems.

• Experience working in Agile development environments and collaborating with cross-functional engineering teams.

• Exposure to production deployments, monitoring, debugging, and maintenance of AI-powered applications.

• Experience contributing to code reviews and following engineering best practices

• Exposure to machine learning model development, evaluation, or deployment in production environments

• Experience applying analytical problem-solving and data-driven decision-making to improve AI solution performance and business outcomes.

Technical / Functional Skills

Mandatory Skills

• Strong analytical thinking and structured problem-solving capabilities — Advanced

• Root cause analysis, debugging, performance optimization, and troubleshooting in AI applications — Advanced

• Ability to break down complex business problems into scalable AI solutions — Advanced

• Understanding of system design fundamentals, engineering trade-offs, and scalability considerations — Intermediate

• Effective use of AI-assisted development tools for coding, debugging, testing, and rapid prototyping — Advanced

• Strong software engineering fundamentals and hands-on development experience — Intermediate

• Code quality practices, unit testing, version control, and CI/CD fundamentals — Advanced

• Large Language Models (LLMs) and AI application development — Advanced

• LangChain and LangGraph — Intermediate to Advanced

• Agentic AI concepts and commonly used agent frameworks — Intermediate

• RAG systems, vector databases, retrieval methods, and context management — Intermediate

• FastAPI-based API development and AI service integration — Intermediate

• Natural Language to SQL and database interaction techniques — Intermediate

• Docker and containerized application development — Intermediate

• Prompt engineering and AI response evaluation techniques — Intermediate to Advanced

• Cloud-based deployment and integration of AI services — Intermediate

• Performance optimization, monitoring, and troubleshooting of AI applications — Intermediate

Preferred (Good to Have) Skills

• Experience with semantic caching and AI response optimization techniques — Intermediate

• Knowledge of prompt optimization frameworks such as DSPy, TextGrad, AdaFlow, or LLMLingua — Basic to Intermediate

• Experience with orchestration and workflow tools for AI deployments — Intermediate

• Familiarity with evaluation frameworks, benchmarking, and AI quality measurement — Intermediate

• Exposure to cloud platforms such as Azure, AWS, or GCP for hosting AI solutions — Intermediate

• Understanding of vector databases, embeddings, and enterprise search systems — Intermediate

And above all of this, an undying love for beer!
We dream big to create future with more cheers.