Senior AI/ML Engineer - R01572485

Brillio 2

Bangalore +1 more

On-site

Who this role is best for

Candidates with AI platform operationalization experience and a background in enterprise architecture will find this role in Bangalore emphasizing technical leadership and collaboration across teams.

Best fit for

Candidates with hands-on AI platform experience and a strategic mindset for governance and MLOps frameworks
— “Define and implement frameworks for model lifecycle management, MLOps/LLMOps, and AI observability”
Individuals with a strong foundation in Python, PySpark, and cloud-based AI platform design
— “Expertise in enterprise-scale AI platform design and operationalization”
Candidates who have led AI solutions and translated technical principles into production-ready systems
— “Collaborate with business, data, technology, and platform teams to translate AI architecture principles into production-ready capabilities”

Things to consider

Commitment to maintaining AI governance and technical guardrails is a key expectation
— “Establish and enforce Responsible AI principles, governance frameworks, and technical guardrails across AI solutions”
Geographic alignment with Bangalore is likely necessary for in-person collaboration
— “Collaborate with business, data, technology, and platform teams”

How to stand out

Highlight experience with KubeFlow and BentoML in your resume and interview responses
— “Hands-on experience with KubeFlow and BentoML for ML pipeline orchestration”
Emphasize your ability to operationalize AI platforms and ensure scalability
— “Lead the design and architecture of enterprise-scale AI platforms, ensuring scalability, security, and operational readiness”
Showcase your work in model validation using Great Expectations and Evidently AI
— “Experience with Great Expectations and Evidently AI for model validation”
Demonstrate your knowledge of AI observability and Responsible AI frameworks
— “Knowledge of Responsible AI, AI governance, model risk, security, and compliance”
Demonstrate your ability to evaluate and integrate emerging AI technologies
— “Evaluate emerging AI technologies and assess their applicability to enterprise AI platforms”

Pace · Fast PacedCollaboration · HighAutonomy · HighDecision Impact · Company

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

What success looks like

designed and operationalized enterprise-scale AI platforms
established governance frameworks
evaluated emerging AI technologies

Typical background

6+ years of experience in AI/ML engineering

Skills & requirements

Required

AI Platform DesignModel Lifecycle ManagementMlopsAI ObservabilityResponsible AI Principles

Preferred

Genai ArchitecturesLarge Language ModelsAI Agents

Stack & domain

Ai/ml EngineeringEnterprise-scale AI PlatformsModel Lifecycle ManagementMlops/llmopsAI ObservabilityResponsible AIAI GovernanceModel RiskSecurityCompliancePythonPysparkCloud-based AI PlatformsModern Data/ai ArchitecturesClassification AlgorithmsDecision TreesSVMRegression AnalysisStatistical AnalysisComputing For Large DatasetsGenai ArchitecturesLarge Language ModelsAI AgentsTensorFlowPyTorchSci-kit LearnCNTKKerasMxnetVector DatabasesAI OrchestrationAI Observability FrameworksResponsible AI FrameworksAI Architecture PatternsAcceleratorsLeadershipCommunicationProblem-solvingTeamworkAdaptabilityOwnershipTensorflow Developer CertificateMicrosoft Certified: Azure AI Engineer AssociateSAS Certified SpecialistIBM Data Science Professional Certificate

About the role

Original posting from Brillio 2 via Lever

Senior AI/ML Engineer
Job requirements:
Experience Range: with at least 6 years of experience in AI/ML engineering, including hands-on expertise in designing, building, and operationalizing enterprise-scale AI platforms Key Responsibilities:
Lead the design and architecture of enterprise-scale AI platforms, ensuring scalability, security, and operational readiness
Define and implement frameworks for model lifecycle management, MLOps/LLMOps, and AI observability to support robust deployment and monitoring
Establish and enforce Responsible AI principles, governance frameworks, and technical guardrails across AI solutions
Drive platform-level technical decision-making and define reusable architecture patterns, standards, and reference models for AI and GenAI solutions
Collaborate with business, data, technology, and platform teams to translate AI architecture principles into production-ready capabilities
Evaluate emerging AI technologies and assess their applicability to enterprise AI platforms, supporting long-term scalability and business outcomes
Maintain and improve AI model governance, version control, and documentation for ongoing operational excellenceRequired Skills:
Expertise in enterprise-scale AI platform design and operationalization
Deep proficiency in model lifecycle management, MLOps/LLMOps, and AI observability
Advanced programming skills in Python and PySpark
Experience with cloud-based AI platforms and modern data/AI architectures
Knowledge of Responsible AI, AI governance, model risk, security, and compliance
Hands-on experience with KubeFlow and BentoML for ML pipeline orchestration
Competence in classification algorithms such as decision trees and SVM
Experience with Great Expectations and Evidently AI for model validation
Strong proficiency in regression analysis (linear and logistic)
Statistical analysis and computing for large datasetsPreferred Skills:
Experience with GenAI architectures, large language models, and AI agents
Proficiency in advanced ML frameworks such as TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, or MXNet
Knowledge of vector databases and AI orchestration
Understanding of AI observability and Responsible AI frameworks
Experience developing reusable AI architecture patterns and acceleratorsDesired Qualifications:
Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, or a closely related discipline
Certification in Machine Learning or Data Science (e.g., TensorFlow Developer Certificate, Microsoft Certified: Azure AI Engineer Associate)
Relevant certification in statistical analysis or advanced analytics (e.g., SAS Certified Specialist, IBM Data Science Professional Certificate)

Source: Brillio 2 careers (Lever)