Senior AI Platform Engineer

Senior AI Platform Engineer building enterprise GenAI, agentic AI, and RAG platforms for EY’s global consulting business. Developing secure cloud-native services and AI integrations.

Posted 8/27/2026full-timeBengaluru • 🇮🇳 IndiaSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and developing enterprise-grade GenAI applications, with strong capabilities in Python, LangChain, and backend engineering. Proficient in implementing scalable AI architectures, CI/CD pipelines, and ensuring compliance with AI governance and security standards.

Highest-signal resume keywords

GenAI Application DevelopmentPython ProgrammingLangChain and LangGraph ProficiencyCI/CD Pipeline ImplementationAI Governance and Compliance

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonGenAILLMsRAG SystemsFastAPIREST APIsVector SearchSQL DatabasesNoSQL DatabasesEmbeddings

Soft Skills

Analytical Problem-SolvingCommunicationStakeholder ManagementCollaborationContinuous Learning

Tools & Technologies

DockerKubernetesOpenShiftGitHub ActionsGitLab CIAzure OpenAIAzure AI ServicesPineconeFAISSRedis Vector

Industry Keywords

AI ArchitectureEnterprise IntegrationsMonitoring and ObservabilityData PrivacyResponsible AI

Tech Stack

Tools & technologies

AzureCloudDockerKubernetesMicroservicesMongoDBNoSQLOpenShiftPythonRedisSQL

About the role

Key responsibilities & impact

Design and develop enterprise-grade GenAI applications using LangChain, LangGraph, AutoGen, Google Agent SDK, and Model Context Protocol
Build and deploy agentic AI architectures, including multi-agent workflows, tool/function calling, enterprise integrations, and autonomous decision-making systems
Develop and maintain RAG pipelines covering document ingestion, chunking, embeddings, vector indexing, retrieval optimization, and response grounding
Implement semantic search and knowledge retrieval using vector databases
Design scalable, reliable, secure, and high-performing AI system architectures
Contribute to AI evaluation, observability, monitoring, and performance optimization
Design and build scalable backend services with Python, FastAPI, REST APIs, microservices, and event-driven architectures
Develop reusable AI platform components, services, APIs, and integrations
Integrate AI solutions with enterprise systems, third-party applications, workflow platforms, and data services
Troubleshoot and optimize AI pipelines, APIs, vector stores, backend services, and cloud-native applications
Deploy and manage applications using Docker, Kubernetes, OpenShift, and cloud-native services
Build and maintain CI/CD pipelines using GitHub Actions, GitLab CI, and modern DevOps tooling
Ensure production readiness through monitoring, observability, automated testing, release management, and operational excellence
Support deployment and lifecycle management across development, testing, staging, and production environments
Implement controls for PII protection, data privacy, AI security, compliance, and responsible AI
Support AI governance through monitoring, auditability, access controls, and compliance frameworks
Collaborate with data engineers, cloud and platform teams, security teams, product owners, and business stakeholders
Participate in architecture, code, testing, and technical design reviews
Support production operations through troubleshooting, performance tuning, root-cause analysis, and continuous improvement

Requirements

What you’ll need

Bachelor’s degree in computer science, Information Technology, Engineering, or a related discipline
4+ years of professional software engineering experience with strong exposure to GenAI, LLMs, platform engineering, and backend development
Strong hands-on expertise in Python for AI application development and backend engineering
Experience building LLM-powered applications, RAG systems, agentic workflows, prompt engineering solutions, and enterprise AI integrations
Hands-on proficiency with LangChain and LangGraph
Exposure to AutoGen, Google Agent SDK, Model Context Protocol (MCP), or skills-based agent frameworks
Experience implementing vector search using Azure AI Search, Pinecone, FAISS, Redis Vector, or pgvector
Strong understanding of embeddings, semantic search, chunking strategies, retrieval optimization, ranking, and context management
Experience developing scalable backend services using FastAPI, REST APIs, microservices, and event-driven architectures
Hands-on experience with Azure OpenAI and Azure AI Services
Knowledge of Docker, with exposure to Kubernetes and OpenShift
Experience implementing CI/CD pipelines using GitHub Actions, GitLab CI, or similar DevOps platforms
Strong understanding of SQL and NoSQL databases including MongoDB, Redis, ClickHouse, and scalable data architectures
Experience supporting enterprise AI systems through monitoring, observability, evaluation, and production operations
Understanding of AI governance, security, privacy, compliance, and responsible AI frameworks
Strong analytical and problem-solving capabilities
Excellent communication and stakeholder management skills
Ability to translate complex business requirements into scalable technical solutions
Strong collaboration skills across engineering, product, and business functions
Commitment to engineering excellence, continuous learning, and innovation

Benefits

Comp & perks

Continuous learning and development opportunities
Tools and flexibility to make a meaningful impact
Coaching and leadership development
Diverse and inclusive culture
Opportunity to work with global clients and well-known brands
Collaboration with AI experts, analytics leaders, and industry specialists
Exposure to projects across multiple client sectors