Data Scientist Generative AI

About InfoBeans

At InfoBeans, our philosophy is simple: we design our company to be our team's second home. We are an AI-first software engineering and digital transformation partner committed to making other people's lives better—through our work, our technology, and our everyday interactions.

Our culture is caring, collaborative, experimental, and ethical. Guided by our core pillars of Excellence, Ownership, Compassion, and Openness, we strive to go beyond the ordinary. Creating WOW! is not just a tagline for us; it's our culture. If you love solving complex software engineering challenges while working in an environment where your voice is heard and your ideas are valued, you'll fit right in.

Role Overview

We are looking for a highly skilled AI Engineer with a strong foundation in software engineering, Python development, and cloud-native application development. The role focuses on building, deploying, and integrating intelligent AI agents powered by Large Language Models (LLMs) into enterprise systems.

The ideal candidate will have hands-on experience developing scalable backend applications, REST APIs, and cloud-based solutions. Candidates with strong software engineering backgrounds and an interest in building production-ready AI applications are encouraged to apply.

Experience: 6–10 years

Key Responsibilities

• AI Application Development: Design, develop, and deploy scalable serverless and containerized AI applications using Python and cloud-native services.
• AI Agent Development: Build intelligent AI agents that use LLMs as reasoning engines and integrate seamlessly with existing production systems.
• API Development: Develop robust RESTful APIs using FastAPI and ensure seamless communication between applications and enterprise systems.
• System Integration: Collaborate with cross-functional teams to integrate AI solutions with internal platforms, enterprise applications, and third-party services.
• Cloud-Native Engineering: Develop and maintain applications using microservices, serverless architectures, containers, and cloud services.
• Performance Optimization: Analyze system performance, identify bottlenecks, and improve application scalability, latency, and reliability.
• Monitoring & Troubleshooting: Monitor AI applications and troubleshoot issues related to connectivity, timeouts, latency, and system integrations.
• AI Observability: Implement monitoring and evaluation practices to track the performance, reliability, and effectiveness of AI-powered systems.
• Continuous Improvement: Apply software engineering best practices, DevOps principles, and automated testing to maintain high-quality production applications.

Required Skills & Qualifications

• Strong understanding of software engineering principles, design patterns, and development best practices.
• Proficiency in Python programming and backend application development.
• Hands-on experience with FastAPI and RESTful API development.
• Experience working with cloud platforms such as AWS or Microsoft Azure.
• Strong understanding of cloud-native architecture, microservices, and serverless computing.
• Knowledge of containerization, deployment practices, and DevOps principles.
• Basic understanding of Large Language Models (LLMs), Generative AI, and MLOps.
• Experience with complex system integrations and connecting applications to enterprise platforms.
• Understanding of application monitoring, troubleshooting, latency, scalability, and reliability.
• Strong analytical, problem-solving, and debugging skills.
• Ability to collaborate effectively with cross-functional teams and stakeholders.
• Good communication skills and the ability to work in a collaborative engineering environment.

Key Skill Areas

• Python & Backend Development: Building scalable backend services and reusable application components.
• FastAPI & REST APIs: Developing reliable APIs for AI applications and enterprise integrations.
• Cloud Platforms: AWS or Azure services, cloud-native architecture, and serverless applications.
• AI Agents & LLMs: Integrating LLMs into applications and developing intelligent agent-based workflows.
• Microservices & System Integration: Connecting AI solutions with enterprise systems and external services.
• Performance Engineering: Troubleshooting latency, timeouts, connectivity, and scalability issues.
• DevOps & Monitoring: Application deployment, observability, automated testing, and production support.

Good to Have

• Experience working with Databricks.
• Experience with AWS services such as Lambda, API Gateway, and Fargate.
• Experience integrating with enterprise platforms such as Snowflake, SAP, ServiceNow, or Salesforce.
• Familiarity with LLM frameworks such as LangChain, LlamaIndex, or DSPy.
• Knowledge of prompt engineering and prompt optimization techniques.
• Understanding of AI agent communication protocols such as A2A and MCP.
• Familiarity with agent evaluation frameworks and testing methodologies.
• Experience with vector databases, embeddings, and retrieval-augmented generation (RAG).
• Awareness of RPA tools such as UiPath or Microsoft Power Automate.
• Experience working in enterprise-level IT environments.