The Role
Lead development of full-stack intelligent applications using Python, JavaScript/TypeScript, cloud platforms, microservices, and agentic AI frameworks. Responsibilities include building multi-agent systems, autonomous workflows, scalable backend architectures, APIs, and RAG solutions; optimizing security, performance, testing, and observability; and collaborating on technical architecture and business requirements.
Summary Generated by Built In
Lead AI Engineer I
Job requirements
- Design and build end-to-end full-stack intelligent applications, integrating frontend, backend, and APIs using modern 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
- 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
- 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
- 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)
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:
Required Skills:
Preferred Skills:
Desired Qualifications:
Additional Information: Location: Bangalore (Hybrid)
Skills Required
- 4 to 6 years of experience in machine learning engineering
- Hands-on experience with cloud-native architectures
- Experience with agentic AI frameworks and scalable full-stack systems
- Advanced proficiency in Python and JavaScript or TypeScript
- Experience with frontend frameworks such as React, Angular, or Vue
- Backend expertise with Node.js, Java Spring Boot, or Python frameworks including FastAPI or Django
- Hands-on experience with Azure, AWS, or GCP
- Proficiency with Docker and basic understanding of Kubernetes
- Experience designing RESTful APIs and microservices
- Experience with LangChain, LlamaIndex, Semantic Kernel, AutoGen, or CrewAI
- Understanding of prompt engineering and Retrieval-Augmented Generation
- Familiarity with CI/CD pipelines
- Experience with autonomous systems or multi-agent architectures
- Knowledge of conversational AI or AI-driven automation workflows
- Familiarity with vector databases such as FAISS or Pinecone
- Expertise with event streaming systems such as Kafka or Pub/Sub
- Contributions to open-source projects or AI/ML or full-stack hackathons
- Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related discipline
- Cloud certification such as AWS Certified Machine Learning Specialty, Azure AI Engineer Associate, or Google Professional Machine Learning Engineer
- Certification in agentic AI frameworks or full-stack development
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.
