Lead AI/ML Engineer - R01572495

Brillio 2
Bangalore +1 more
On-site

Who this role is best for

Candidates with Azure-based AI/ML experience and a background in enterprise systems integration will find this role in Bangalore emphasizing agentic AI and LLMs for scalable deployment.

Best fit for

  • Candidates with 4-6 years of experience in Azure AI and agentic systems for enterprise platforms
    — “at least 4 to 6 years of experience designing, building, and operationalizing enterprise-scale AI platforms”
  • Professionals skilled in deploying AI agents and integrating with Microsoft 365 and enterprise APIs
    — “Develop and operationalize agentic workflows for business functions, integrating AI solutions with Microsoft 365, Teams, SharePoint, Dynamics 365, Power Platform, and enterprise APIs”
  • Individuals who have developed production-ready AI applications with LLMs and RAG pipelines
    — “Build production-ready AI applications leveraging LLMs, RAG pipelines, tool/function calling, memory systems, and workflow orchestration”

Things to consider

  • The role requires a strong grasp of Azure AI and agentic frameworks, which may demand specialized cloud and AI knowledge.
    — “hands-on expertise in agentic AI, LLMs, and Azure-based solutions”
  • Experience with model evaluation and observability tools is a critical requirement for deploying AI applications.
    — “Establish observability, guardrails, responsible AI controls, evaluation, security, and monitoring for AI applications”

How to stand out

  • Demonstrate hands-on experience with Azure AI Search and Semantic Kernel in your resume and interview examples.
    — “Implement enterprise RAG solutions utilizing Azure AI Search, vector search, hybrid search, and semantic ranking”
  • Highlight your ability to optimize AI applications for latency, accuracy, and cost in your interview responses.
    — “Optimize agent and LLM applications for latency, accuracy, reliability, scalability, security, and cost”
  • Showcase your work with cross-functional teams and rapid prototyping in your portfolio or resume.
    — “Collaborate with cross-functional teams to identify AI opportunities, rapidly prototype solutions”
  • Emphasize your experience with AI workflow orchestration tools such as KubeFlow and BentoML.
    — “AI workflow orchestration tools (KubeFlow, BentoML)”
  • Include specific examples of deploying AI applications with enterprise-grade security and governance.
    — “Establish observability, guardrails, responsible AI controls, evaluation, security, and monitoring”
Pace · SteadyCollaboration · HighAutonomy · HighDecision Impact · Company

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • successful deployment of AI agents
  • enhanced knowledge retrieval
  • reliable enterprise deployment
  • established observability and security controls
Typical background
4-6 years of experience in AI/MLcertifications in ML or Data Science

Skills & requirements

Required

Enterprise-scale AI PlatformsAzure AI ServicesAgentic AILlmsAzure OpenaiSemantic KernelLangchainLanggraphAutogenEnterprise RAG SolutionsAzure AI SearchVector SearchHybrid SearchSemantic RankingAI Workflow OrchestrationModel EvaluationObservabilitySecurityMonitoringAI Engineering Standards

Preferred

Azure FunctionsAzure Container AppsAzure Kubernetes Service (aks)Azure StorageR And R StudioSAS Or SPSSMicroservicesEvent-driven ArchitecturesEnterprise IntegrationsReusable Agent FrameworksPrompt LibrariesDeployment Patterns

Stack & domain

Azure AI ServicesAzure OpenaiSemantic KernelLangchainLanggraphAutogenAzure AI SearchVector SearchHybrid SearchSemantic RankingLlmsRAG PipelinesTool/function CallingMemory SystemsWorkflow OrchestrationObservabilityGuardrailsResponsible AI ControlsEvaluationSecurityMonitoringAzure FunctionsAzure Container AppsAzure Kubernetes Service (aks)Azure StorageRR StudioSASSPSSMicroservicesEvent-driven ArchitecturesEnterprise IntegrationsReusable Agent FrameworksPrompt LibrariesDeployment PatternsTensorFlowPyTorchSci-kit LearnKerasAI Workflow Orchestration ToolsModel Evaluation And Observability ToolsAI Security And Governance On AzureCollaborationTeamworkCommunicationProblem-solvingInnovationLeadershipProject ManagementTime ManagementAdaptabilityCuriosityIntegrityTensorflow Developer CertificateAWS Certified Machine Learning SpecialtyMicrosoft Certified: Azure AI Engineer AssociateSAS Certified Statistical Business AnalystAIMLEnterpriseAgentic AIAzureMicrosoft 365TeamsSharepointDynamics 365Power PlatformEnterprise ApisPythonStatisticsData ScienceAI Engineering

About the role

Original posting from Brillio 2 via Lever

Lead AI/ML Engineer

Job requirements:

Experience Range: With at least 4 to 6 years of experience designing, building, and operationalizing enterprise-scale AI platforms, including hands-on expertise in agentic AI, LLMs, and Azure-based solutions. Key Responsibilities:

  • Design, build, and deploy AI agents and multi-agent systems using Azure AI services, Azure OpenAI, and modern agentic frameworks such as Semantic Kernel, LangChain, LangGraph, and AutoGen.
  • Develop and operationalize agentic workflows for business functions, integrating AI solutions with Microsoft 365, Teams, SharePoint, Dynamics 365, Power Platform, and enterprise APIs.
  • Implement enterprise RAG solutions utilizing Azure AI Search, vector search, hybrid search, and semantic ranking to enhance knowledge retrieval and business decision-making.
  • Build production-ready AI applications leveraging LLMs, RAG pipelines, tool/function calling, memory systems, and workflow orchestration for scalable, secure, and reliable enterprise deployment.
  • Establish observability, guardrails, responsible AI controls, evaluation, security, and monitoring for AI applications in production environments.
  • Optimize agent and LLM applications for latency, accuracy, reliability, scalability, security, and cost, applying advanced techniques and metrics.
  • Collaborate with cross-functional teams to identify AI opportunities, rapidly prototype solutions, and iterate based on user feedback.
  • Contribute to internal best practices around agent architecture, prompting, RAG, model selection, evaluation, and AI engineering standards.Required Skills:
  • Hands-on experience with Azure AI services, Azure OpenAI, and Azure AI Foundry
  • Expertise in agentic frameworks (Semantic Kernel, LangChain, LangGraph, AutoGen)
  • LLM application development and prompt engineering
  • Enterprise RAG architectures and vector search technologies
  • Integration with Microsoft 365, Teams, SharePoint, Dynamics 365, Power Platform, and APIs
  • Python programming for AI/ML solutions
  • ML frameworks (TensorFlow, PyTorch, Sci-Kit Learn, Keras)
  • AI workflow orchestration tools (KubeFlow, BentoML)
  • Model evaluation and observability tools (Evidently AI, Great Expectations)
  • AI security and governance on AzurePreferred Skills:
  • Experience with Azure Functions, Azure Container Apps, Azure Kubernetes Service (AKS), and Azure Storage
  • Proficiency in R and R Studio for statistical computing
  • Knowledge of SAS or SPSS for advanced statistical modeling
  • Familiarity with microservices, event-driven architectures, and enterprise integrations
  • Expertise in developing reusable agent frameworks, prompt libraries, and deployment patternsDesired Qualifications:
  • Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, or a closely related discipline
  • Certification in Machine Learning or Data Science from a recognized institution (e.g., TensorFlow Developer Certificate, AWS Certified Machine Learning Specialty, Microsoft Certified: Azure AI Engineer Associate)
  • Certification in statistical analysis tools or platforms (e.g., SAS Certified Statistical Business Analyst)

Source: Brillio 2 careers (Lever)

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