AI Engineer

Pune · Punemid

Data ScienceAI EngineerData & AI

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Quick Summary

Key Responsibilities

Design, develop, and maintain scalable backend services using Node.js and .NET Core Build and operate cloud-native applications within AWS (e.g., Lambda, ECS/EKS, S3,

Requirements Summary

3–6 years of professional software engineering experience Strong development experience in Node.js (JavaScript/TypeScript) and .

Technical Tools

Data ScienceAI EngineerData & AI

Responsibilities

~1 min read

→Design, develop, and maintain scalable backend services using Node.js and .NET Core
→Build and operate cloud-native applications within AWS (e.g., Lambda, ECS/EKS, S3, API Gateway)
→Integrate AI/ML models into production systems, including APIs and data pipelines
→Participate in model evaluation, benchmarking, and performance analysis
→Contribute to fine-tuning and optimization of AI models for specific business use cases
→Collaborate with data scientists and AI engineers to productionize models
→Implement and maintain CI/CD pipelines and deployment workflows
→Ensure high standards of code quality, testing, and observability
→Troubleshoot, debug, and optimize system performance
→Contribute to architectural decisions and continuous improvement initiatives

Requirements

~1 min read

3–6 years of professional software engineering experience
Strong development experience in Node.js (JavaScript/TypeScript) and .NET Core / C#
Hands-on experience with AWS cloud services
Experience building and consuming RESTful APIs and/or microservices
Familiarity with containerization (Docker) and orchestration (Kubernetes or ECS)
Solid understanding of software engineering principles, design patterns, and testing practices
Practical experience working with AI/ML systems in production environments
Understanding of model evaluation techniques and fine-tuning approaches
Experience working with LLMs or modern AI frameworks (e.g., OpenAI, Hugging Face, LangChain, Semantic Kernel) is highly desirable
Familiarity with vector databases, embeddings, or RAG architectures is a plus
Strong problem-solving and analytical thinking
Ability to work collaboratively in a cross-functional team
Good communication skills, with the ability to explain technical concepts clearly
Self-motivated with a willingness to learn and adapt in a rapidly evolving AI landscape