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Project ManagerJob requirementsExperience Range with at least 7 years of experience in workforce management, real-time analysis, or contact center operations, including experience supporting daily op....
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Lead DesignJob requirementsExperience Range With at least 4 years of hands-on UX design experience, including up to 6 years leading end-to-end design projects Key ResponsibilitiesLead the design pro....
Bangalore, India
Lead Quality EngineerJob requirementsRole Overview We are looking for an Automation Tester to support the Abbott AEO/GEO programme by building automated validation frameworks for website quality, stru....
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Senior Engineer, Applications & PlatformsJob requirementsExperience Range 7+ years of hands-on experience in integration middleware platforms, including Mulesoft, Boomi, Workato, or similar tools....
Bangalore, India
Senior Engineer, Applications & PlatformsJob requirementsExperience Range 7 + years of hands-on experience in integration middleware platforms, including Mulesoft, Boomi, Workato, or similar tool....
Bangalore, India
Senior Engineer, Applications & PlatformsJob requirementsExperience Range With at least 7 + years of hands-on experience in integration middleware engineering, specifically working with platforms....
A self-service AI model garden that sows the seeds of innovation
A self-service LLM Model Garden and GenAI platform for building, testing, and deploying over a hundred GenAI use cases at scale.
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12 questions
Brillio is The Enterprise AI Accelerator, helping Fortune 1000 companies move from AI ambition to scaled impact. It delivers transformation powered by its AI Accelerator Platform, ADAM. Brillio delivers across five core workstreams: business-led transformation, customer experience transformation, AI and data engineering, digital engineering, and infrastructure engineering, combining industry expertise, modern engineering, and accelerators to deliver measurable outcomes.
Brillio invests in continuous learning through Brillio Academy, alongside mentoring, hackathons, and a personalized career development framework.
Engineers ship enterprise AI into production for real clients, not sandbox demos. Most work end to end: designing, building, evaluating, and running agentic and GenAI systems that reach live users.
No. Hiring is based on demonstrated ability. Strong software fundamentals, hands-on projects with LLMs, RAG, or agents, and clear reasoning about trade-offs matter more than a specific credential.
Engineers work on current agentic and GenAI problems, not legacy backlogs, with exposure to platforms like ADAM. Learning programs, hackathons, and certifications keep skills close to what's actually changing.
Both. Some engagements are net-new agentic builds; others modernize existing estates. The mix is transparent, so engineers can choose problems that match how they want to grow.
Common tracks run from engineer to AI architect, platform lead, and technical leadership. Growth follows demonstrated impact, not tenure, and staying deeply technical is a valid long-term path.
Interviews cover fundamentals plus applied depth: LLMs, RAG, agents, evaluation, and a design or debugging exercise. Candidates should be ready to walk through a real system they built and shipped.
Engineers get meaningful ownership within clear guardrails, making architecture and trade-off calls on their workstreams. Mentorship is available rather than rigid, top-down prescriptions on how to solve problems.
AI engineers build intelligent applications, predictive models, enterprise search solutions, RAG systems, AI copilots, autonomous agents, and multi-agent workflows. The focus is increasingly on moving AI from prototypes into scalable production systems. At Brillio, engineers solve business challenges across product engineering, application modernization, data, operations, and agentic AI using platforms such as ADAM.
Build strong software and machine learning fundamentals, then create hands-on projects using LLMs, RAG, agents, orchestration frameworks, APIs, and cloud platforms. Add evaluation, monitoring, security, and human oversight to understand production requirements. At Brillio, professionals gain practical exposure through enterprise AI projects, learning programs, hackathons, and opportunities to build agentic solutions using ADAM.
Future-ready AI engineers will need expertise in LLMs, agentic systems, orchestration, data engineering, cloud infrastructure, MLOps, LLMOps, evaluation, observability, security, governance, and responsible AI. Strong software engineering and problem-solving will remain essential. Engineers must also understand how to build reliable systems around probabilistic models and integrate them safely into enterprise applications and workflows.