The Role
Design and develop full-stack AI applications across frontend, backend, and APIs. Build agentic AI systems using multi-agent frameworks, LLM orchestration, RAG, microservices, event-driven architectures, and cloud platforms. Deploy scalable solutions using Docker, Kubernetes, and CI/CD while ensuring quality, security, observability, and performance. Collaborate cross-functionally on architecture and technical delivery.
Summary Generated by Built In
Analyst
Job requirements
- 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
- 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
- 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
- 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)
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:
Required Skills:
Preferred Skills:
Desired Qualifications:
Additional Information: Location: Bangalore (Hybrid work arrangement)
Skills Required
- At least 2 to 4 years of experience in full-stack data science, including cloud-native architectures and agentic AI frameworks
- Frontend development experience with React, Angular, or Vue
- Backend development experience with Node.js, Java Spring Boot, or Python frameworks such as FastAPI or Django
- RESTful API design and microservices architecture experience
- Experience with Azure, AWS, and Google Cloud Platform
- Hands-on experience with Docker containers and Kubernetes
- CI/CD pipeline implementation experience
- Experience with agentic AI frameworks such as LangChain, LlamaIndex, Semantic Kernel, AutoGen, or CrewAI
- Prompt engineering and Retrieval-Augmented Generation experience
- Familiarity with vector databases such as FAISS or Pinecone
- Knowledge of event streaming systems such as Kafka or Pub/Sub
- Experience with federated learning or privacy-preserving algorithms
- Contributions to open-source projects or AI/ML or full-stack hackathons
- Experience with Domino Datalab or Databricks ML
- Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, or a related discipline
- Cloud certification such as AWS Certified Machine Learning Specialty, Azure AI Engineer Associate, or Google Cloud Professional Machine Learning Engineer
- Full-stack development or AI framework certification
Brillio Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Brillio and has not been reviewed or approved by Brillio.
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Healthcare Strength — Healthcare is considered comprehensive, including medical coverage for employees and dependents alongside life, disability, and accidental death protections. Feedback suggests these protections are a core strength of the package.
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Leave & Time Off Breadth — Time-off options include paid leave and parental leave, with flexible or ‘flexible PTO’ approaches cited in some contexts. Feedback suggests this breadth helps support work-life balance when team norms permit usage.
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Wellbeing & Lifestyle Benefits — Wellbeing offerings span counseling, financial-management sessions, fitness programs, and travel insurance, plus region-specific extras like discounted IT hardware and work-from-home essentials. Feedback suggests these add-ons enhance perceived value beyond core insurance.
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The Company
What We Do
Brillio is the leader in global digital business transformation, applying technology with a human touch. We help businesses define internal and external transformation objectives, and translate those objectives into actionable market strategies using proprietary technologies. With 2600+ experts and 13 offices worldwide, Brillio is the ideal partner for enterprises that want to quickly increase their core business productivity, and achieve a competitive edge, with the latest digital solutions.
