Lead AI Engineer

Brillio

. Design and build end-to-end full-stack intelligent applications integrating frontend, backend, and APIs .

Posted 9/17/2026full-timeBangalore • IndiaSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in developing and deploying cloud-native solutions, building scalable full-stack intelligent applications, and implementing agentic AI frameworks. Proficient in optimizing systems for performance and security while ensuring code quality and collaboration with cross-functional teams.

Highest-signal resume keywords

Machine Learning EngineeringCloud-Native ArchitecturesPython ProgrammingAgentic AI FrameworksMicroservices Design

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

PythonJavaScriptTypeScriptReactAngularVueNode.jsJava Spring BootFastAPIDjango

Tools & Technologies

AzureAWSGCPDockerKubernetesRESTful APICI/CD PipelinesLangChainLlamaIndexKafka

Certifications & Qualifications

Bachelor's Degree in Computer ScienceBachelor's Degree in Artificial IntelligenceBachelor's Degree in Machine LearningBachelor's Degree in Data Science

Industry Keywords

Agentic AIMulti-Agent SystemsAutonomous WorkflowsEvent-Driven ArchitecturesPerformance OptimizationObservabilityPrompt EngineeringRetrieval-Augmented GenerationVector DatabasesConversational AI

Tech Stack

Tools & technologies

AngularAWSAzureCloudDjangoDockerGoogle Cloud PlatformJavaJavaScriptKafkaKubernetesMicroservicesNode.jsPythonReactSpringSpring BootSpringBootTypeScriptVue.js

About the role

Key responsibilities & impact

Design and build end-to-end full-stack intelligent applications integrating frontend, backend, and APIs
Develop and deploy cloud-native solutions using 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
Optimize systems for performance, security, and scalability
Use LLM-based frameworks and agent orchestration tools to create intelligent, adaptive workflows
Ensure code quality, testing, debugging, observability, and performance optimization best practices
Collaborate with cross-functional teams to translate business requirements into robust technical solutions
Participate in architectural decisions

Requirements

What you’ll need

At least 4 to 6 years of experience in ML engineering
Hands-on experience with cloud-native architectures, agentic AI frameworks, and scalable full-stack systems
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 Azure, AWS, and GCP
Proficiency in Docker and basic understanding of Kubernetes
RESTful API and microservices design experience
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
Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a closely related discipline (desired)
Preferred experience with autonomous systems or multi-agent architectures
Preferred knowledge of conversational AI or AI-driven automation workflows
Preferred familiarity with vector databases such as FAISS or Pinecone
Preferred expertise in event streaming systems like Kafka or Pub/Sub
Contributions to open-source projects or hackathons in AI/ML or full stack domains are preferred

Benefits

Comp & perks

Hybrid work arrangement
Cloud platform certification opportunities (e.g., AWS Certified Machine Learning Specialty, Azure AI Engineer Associate, Google Professional Machine Learning Engineer)
Relevant certification opportunities in agentic AI frameworks or full-stack development