AIA - AI Engineer

Job Summary

The opportunity: We are seeking a dynamic Senior consultant to join our AI & Data Consulting team, focused on building scalable, enterprise-grade GenAI and Agentic AI solutions. The ideal candidate will bring a solid combination of AI engineering, platform development, cloud-native architecture, and backend engineering expertise, along with the ability to collaborate across cross-functional teams to deliver secure, scalable, and high-performing AI applications.

You will work closely with data engineers, cloud architects, platform teams, security teams, product owners, and business stakeholders to design and implement LLM-powered platforms, agentic AI systems, Retrieval-Augmented Generation (RAG) solutions, and enterprise AI services that accelerate innovation and business transformation.

Responsibilities

Technical Excellence: AI Engineering & Agentic AI Development

- Design and develop enterprise-grade GenAI applications leveraging LLM frameworks such as LangChain, LangGraph / AutoGen / Google Agent SDK, and Model Context Protocol (MCP).
- Build and deploy agentic AI architectures, including multi-agent workflows, tool/function calling, enterprise integrations, and autonomous decision-making systems.
- Develop and maintain Retrieval-Augmented Generation (RAG) pipelines, including document ingestion, chunking, embeddings generation, vector indexing, retrieval optimization, and response grounding.
- Implement semantic search and knowledge retrieval solutions using vector databases such as Azure AI Search, Pinecone, FAISS, Redis Vector, and pgvector.
- Design robust AI system architectures that ensure scalability, reliability, security, and performance.
- Contribute to AI evaluation, observability, monitoring, and performance optimization of LLM-powered applications.
- Stay current with emerging trends in GenAI, Agentic AI, multimodal AI, enterprise AI platforms,



and AI engineering practices.

Backend & Platform Engineering

- Design and build scalable backend services using Python, FastAPI, REST APIs, microservices, and event-driven architectures.
- Develop reusable AI platform components, services, APIs, and integrations to accelerate enterprise AI adoption.
- 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.
- Implement scalable deployment strategies using containerized and cloud-native architectures.

Cloud, Infrastructure & DevOps

- Develop enterprise AI solutions using Azure OpenAI, Azure AI Services, and cloud-native services.
- Deploy and manage applications using Docker, Kubernetes, OpenShift, and container orchestration platforms.
- 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.

AI Governance, Security & Responsible AI

- Implement enterprise controls for PII protection, data privacy, AI security, compliance, and responsible AI practices.
- Support AI governance initiatives through monitoring, auditability, access controls, and compliance frameworks.
- Contribute to AI observability practices,



including monitoring model behavior, hallucination risks, accuracy, latency, and retrieval quality.
- Ensure adherence to enterprise architecture, security standards, and engineering best practices.

Team Collaboration & Delivery Excellence

- Collaborate with data engineers, cloud and platform teams, security teams, product owners, and business stakeholders to refine requirements and deliver scalable AI solutions.
- Participate in architecture reviews, code reviews, testing reviews, and technical design discussions.
- Drive engineering excellence through reusable components, documentation, automation, and quality standards.
- Support production operations including troubleshooting, performance tuning, root-cause analysis, and continuous improvement.

Skills and Qualifications

Educational Background

- Bachelor's degree in computer science, information technology, engineering, or a related discipline.

Professional Experience

- 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, LangGraph, and exposure to AutoGen, Google Agent SDK, Model Context Protocol (MCP), or skills-based agent frameworks.
- Experience implementing vector search solutions using Azure AI Search, Pinecone, FAISS, Redis Vector, or pgvector.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 EY - GDS Consulting - AIA - AI Engineer- Senior (Bengaluru)
🏢 EY
📍 Bengaluru