The Lead AI Engineer will design and build full-stack, cloud-native AI applications that incorporate multi-agent and LLM-based workflows. They will implement scalable microservices, event-driven backends, and ensure robust deployment on Azure, AWS, or GCP.
Experience Range: With at least 2 to 4 years of experience in full-stack data science, including practical exposure to cloud-native architectures and agentic AI frameworks Key Responsibilities:
Design and develop end-to-end full-stack applications, covering frontend, backend, and API components
Build and deploy agentic AI applications using multi-agent systems and autonomous workflows
Implement scalable backend systems with microservices and event-driven architectures to support intelligent solutions
Develop cloud-native solutions on platforms such as Azure, AWS, and GCP, ensuring robust and scalable deployments
Integrate LLM-based frameworks and agent orchestration tools to enable adaptive and intelligent workflows
Enforce best practices in code quality, testing, debugging, observability, performance optimization, security, and scalability
Collaborate with cross-functional teams to deliver technical solutions aligned with business requirements
Contribute to design reviews and architectural decisions for AI-driven systems
Required Skills:
Full-stack development with React, Angular, or Vue for frontend
Backend development using Node.js, Java Spring Boot, or Python frameworks (FastAPI, Django)
RESTful API design and microservices architecture
Experience with Azure, AWS, and GCP cloud platforms
Hands-on experience with Docker containers and Kubernetes
CI/CD pipeline implementation
Agentic AI frameworks such as LangChain, LlamaIndex, Semantic Kernel, AutoGen, or CrewAI
Prompt engineering and Retrieval-Augmented Generation (RAG) techniques
Preferred Skills:
Familiarity with vector databases such as FAISS or Pinecone
Knowledge of event streaming systems like Kafka or Pub/Sub
Experience with federated learning or privacy-preserving algorithms
Contributions to open-source projects or hackathons in AI/ML or full-stack domains
Experience with model experimentation platforms such as Domino Datalabs or Databricks ML
Desired Qualifications:
Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, or a closely related discipline
Certification in cloud platforms (e.g., AWS Certified Machine Learning Specialty, Azure AI Engineer Associate, Google Cloud Professional Machine Learning Engineer)
Certification in full-stack development or AI frameworks (e.g., Full Stack Web Development, TensorFlow Developer Certificate)
Additional Information: Location: Bangalore (Hybrid work arrangement)
