Lead AI Engineer I

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Job requirements

Experience Range: With at least 4 to 6 years of experience in ML engineering, including hands-on work with cloud-native architectures, agentic AI frameworks, and scalable full-stack systems Key Responsibilities:
- Design and build end-to-end full-stack intelligent applications, integrating frontend, backend, and APIs using contemporary frameworks
- Develop and deploy cloud-native solutions leveraging platforms such as Azure, AWS, and GCP
- Build and implement agentic AI applications, including multi-agent systems and autonomous workflows
- Develop scalable backend systems using microservices and event-driven architectures, optimizing for performance, security, and scalability
- Work with LLM-based frameworks and agent orchestration tools to create intelligent, adaptive workflows
- Ensure best practices in code quality, testing, debugging, observability, and performance optimization
- Collaborate with cross-functional teams to translate business requirements into robust technical solutions and participate in architectural decisions
Required Skills:
- Advanced proficiency in Python and JavaScript/TypeScript
- Experience with frontend frameworks such as React, Angular, or Vue
- Backend expertise with Node.js, Java Spring Boot, or Python (FastAPI, Django)
- Hands-on experience with cloud platforms (Azure,



AWS, GCP)
- Proficiency in containers (Docker) and basic understanding of Kubernetes
- RESTful API and microservices design
- Experience with agentic AI frameworks such as LangChain, LlamaIndex, Semantic Kernel, AutoGen, or CrewAI
- Understanding of prompt engineering and Retrieval-Augmented Generation (RAG)
- Familiarity with CI/CD pipelines
Preferred Skills:
- Experience with autonomous systems or multi-agent architectures
- Knowledge of conversational AI or AI-driven automation workflows
- Familiarity with vector databases (FAISS, Pinecone, etc.)
- Expertise in event streaming systems like Kafka or Pub/Sub
- Contributions to open-source projects or hackathons in AI/ML or full stack domains
Desired Qualifications:
- Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a closely related discipline
- Certification in cloud platforms (e.g., AWS Certified Machine Learning Specialty, Azure AI Engineer Associate, Google Professional Machine Learning Engineer)
- Relevant certification in agentic AI frameworks or full-stack development (e.g., TensorFlow Developer Certificate, React Professional Certification)
Additional Information: Location: Bangalore (Hybrid)

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📌 Lead AI Engineer I162 (Bengaluru)
🏢 Brillio
📍 Bengaluru