Senior AI Engineer I

American Express; Bengaluru; Hybrid; INR 4,500,000 - 6,500,000; Full time

Benefits offered by this job

Bonus incentives

Flexible working model

Medical benefits

Job summary

American Express is seeking a highly skilled Senior AI Engineer to lead the design, development, deployment, and scaling of enterprise-grade Agentic AI solutions on Google Cloud Platform (GCP). You will design, build, deploy, and scale AI pipelines and multi-agent systems, collaborating with world-class engineers, data scientists, and product teams to deliver scalable enterprise AI capabilities.

The role focuses on GenAI, LangGraph workflows, distributed data engineering, and cloud-native

Qualifications

• Bachelor's or master's degree in computer science, engineering, or related field.
• 8+ years of software engineering experience with AI/ML or GenAI engineering.
• Strong experience building production systems in Python.
• Hands-on experience with LangGraph, LangChain, CrewAI, AutoGen, or equivalent agent orchestration frameworks.
• Experience deploying applications on Google Cloud Platform (GCP).
• Strong background in data engineering, distributed systems, and large-scale data processing.
• Experience with BigQuery, Dataflow, Dataproc, Pub/Sub, Cloud Storage, and GKE.
• Experience building RAG, Vector Search, and Knowledge Graph solutions.
• Experience with Kubernetes, Docker, CI/CD, and infrastructure automation.

Responsibilities

• Design, develop, and deploy enterprise-scale Agentic AI applications using LangGraph, LangChain, and modern LLM frameworks.
• Build and optimize multi-agent workflows including routing, planning, reasoning, memory, validation, and execution agents.
• Develop production-grade agent orchestration frameworks capable of handling complex business processes.
• Implement workflow state management, checkpointing, human-in-the-loop controls, and failure recovery mechanisms.
• Design intelligent agent collaboration patterns including supervisor-agent, planner-executor, and hierarchical agent architectures.
• Create reusable AI skills, tools, prompts, and workflow libraries that accelerate enterprise AI adoption.
• Build scalable AI services and APIs deployed on GKE (Google Kubernetes Engine).
• Design containerized AI workloads using Kubernetes, Helm, Docker, and GitOps deployment practices.
• Implement CI/CD pipelines for Agentic AI and GenAI applications.
• Optimize inference latency, throughput, resource utilization, and operational costs across AI workloads.
• Support production deployment, monitoring, observability, and incident management for AI systems.
• Design resilient and highly available AI infrastructure supporting enterprise SLAs.
• Build distributed data pipelines using Apache Beam, Spark, Dataproc, Dataflow, and BigQuery.
• Design batch and streaming ingestion frameworks using Pub/Sub and event-driven architectures.
• Develop scalable ETL/ELT pipelines supporting AI and analytics workloads.
• Implement enterprise data processing frameworks for structured, semi-structured, and unstructured data.
• Build data quality, lineage, metadata, and governance capabilities across AI pipelines.
• Optimize BigQuery and storage architectures for performance and cost efficiency.
• Design and implement Retrieval Augmented Generation (RAG) platforms.
• Build vector search and semantic retrieval solutions using enterprise knowledge repositories.
• Develop document ingestion, indexing, chunking, embedding, and retrieval pipelines.
• Implement hybrid search architectures combining vector, keyword, and graph-based retrieval capabilities.
• Build knowledge graphs and context-management systems supporting intelligent agents.
• Design AI systems capable of processing millions of records and large-scale enterprise datasets.
• Improve workflow reliability through distributed execution, workload partitioning, and fault-tolerant designs.
• Implement asynchronous processing using Pub/Sub, event-driven architectures, and worker-based execution models.
• Build performance monitoring, tracing, and observability frameworks using OpenTelemetry and cloud-native monitoring tools.
• Conduct load testing, performance tuning, and capacity planning activities.
• Implement model governance, auditability, explainability, and compliance controls.
• Develop automated validation and confidence-scoring frameworks for GenAI outputs.
• Establish evaluation pipelines for model quality, hallucination detection, and business-rule validation.
• Support secure and compliant use of enterprise data in AI systems.
• Partner with governance and risk teams to align AI solutions with enterprise standards.
• Lead architecture reviews and provide technical direction across AI engineering initiatives.
• Mentor junior engineers and establish engineering best practices.
• Drive innovation in Agentic AI, GenAI, LLMOps, and cloud-native engineering.
• Collaborate with product, business, and enterprise architecture teams to deliver strategic AI capabilities.
• Contribute to enterprise AI platforms, reusable frameworks, and long-term technology roadmaps.

Skills

AI/ML engineering

Python development

LangGraph/LangChain

GCP deployment

Distributed systems

Education

Bachelor's or master's degree in CS/Engineering

Tools

LangGraph

LangChain

CrewAI

AutoGen

Kubernetes

Docker

BigQuery

Dataflow

Dataproc

Job description

Job Description

You Lead the Way. We've Got Your Back.

With the right backing, people and businesses have the power to progress in incredible ways. When you join Team Amex, you become part of a global and diverse community committed to delivering innovative customer experiences through cutting-edge technology and AI.

At American Express, you will work on next-generation Agentic AI platforms, large-scale data and AI systems, and cloud-native engineering solutions that power intelligent business processes across the enterprise. You will collaborate with world-class engineers, data scientists, architects, and product teams to design, build, deploy, and scale mission-critical AI solutions.

Join Team Amex and help us shape the future of Enterprise AI. We are seeking a highly skilled Senior AI Engineer to lead the design, development, deployment, and scaling of enterprise-grade Agentic AI solutions on Google Cloud Platform (GCP).

This role combines expertise across GenAI, LangGraph workflow orchestration, distributed data engineering, cloud-native architectures, and MLOps. The ideal candidate will build production-ready AI systems capable of processing large-scale enterprise data while ensuring reliability, governance, observability, and performance.

The engineer will play a critical role in establishing scalable AI pipelines, multi-agent architectures, retrieval systems, and intelligent workflow orchestration capabilities that drive business outcomes across the organization.

Responsibilities

Agentic AI & LangGraph Development

• Design, develop, and deploy enterprise-scale Agentic AI applications using LangGraph, LangChain, and modern LLM frameworks.
• Build and optimize multi-agent workflows including routing, planning, reasoning, memory, validation, and execution agents.
• Develop production-grade agent orchestration frameworks capable of handling complex business processes.
• Implement workflow state management, checkpointing, human-in-the-loop controls, and failure recovery mechanisms.
• Design intelligent agent collaboration patterns including supervisor-agent, planner-executor, and hierarchical agent architectures.
• Create reusable AI skills, tools, prompts, and workflow libraries that accelerate enterprise AI adoption.

AI Platform Engineering & Deployment

• Build scalable AI services and APIs deployed on GKE (Google Kubernetes Engine).
• Design containerized AI workloads using Kubernetes, Helm, Docker, and GitOps deployment practices.
• Implement CI/CD pipelines for Agentic AI and GenAI applications.
• Optimize inference latency, throughput, resource utilization, and operational costs across AI workloads.
• Support production deployment, monitoring, observability, and incident management for AI systems.
• Design resilient and highly available AI infrastructure supporting enterprise SLAs.

Data Engineering on GCP

• Build distributed data pipelines using Apache Beam, Spark, Dataproc, Dataflow, and BigQuery.
• Design batch and streaming ingestion frameworks using Pub/Sub and event-driven architectures.
• Develop scalable ETL/ELT pipelines supporting AI and analytics workloads.
• Implement enterprise data processing frameworks for structured, semi-structured, and unstructured data.
• Build data quality, lineage, metadata, and governance capabilities across AI pipelines.
• Optimize BigQuery and storage architectures for performance and cost efficiency.

Retrieval & Knowledge Systems

• Design and implement Retrieval Augmented Generation (RAG) platforms.
• Build vector search and semantic retrieval solutions using enterprise knowledge repositories.
• Develop document ingestion, indexing, chunking, embedding, and retrieval pipelines.
• Implement hybrid search architectures combining vector, keyword, and graph-based retrieval capabilities.
• Build knowledge graphs and context-management systems supporting intelligent agents.

Scalability & Reliability Engineering

• Design AI systems capable of processing millions of records and large-scale enterprise datasets.
• Improve workflow reliability through distributed execution, workload partitioning, and fault-tolerant designs.
• Implement asynchronous processing using Pub/Sub, event-driven architectures, and worker-based execution models.
• Build performance monitoring, tracing, and observability frameworks using OpenTelemetry and cloud-native monitoring tools.
• Conduct load testing, performance tuning, and capacity planning activities.

AI Governance & Responsible AI

• Implement model governance, auditability, explainability, and compliance controls.
• Develop automated validation and confidence-scoring frameworks for GenAI outputs.
• Establish evaluation pipelines for model quality, hallucination detection, and business-rule validation.
• Support secure and compliant use of enterprise data in AI systems.
• Partner with governance and risk teams to align AI solutions with enterprise standards.
• Lead architecture reviews and provide technical direction across AI engineering initiatives.
• Mentor junior engineers and establish engineering best practices.
• Drive innovation in Agentic AI, GenAI, LLMOps, and cloud-native engineering.
• Collaborate with product, business, and enterprise architecture teams to deliver strategic AI capabilities.
• Contribute to enterprise AI platforms, reusable frameworks, and long-term technology roadmaps.

Qualifications

• Bachelor's or master's degree in computer science, Engineering, or related field.
• 8+ years of software engineering experience with AI/ML or GenAI engineering.
• Strong experience building production systems in Python.
• Hands-on experience with LangGraph, LangChain, CrewAI, AutoGen, or equivalent agent orchestration frameworks.
• Experience deploying applications on Google Cloud Platform (GCP).
• Strong background in data engineering, distributed systems, and large-scale data processing.
• Experience with BigQuery, Dataflow, Dataproc, Pub/Sub, Cloud Storage, and GKE.
• Experience building RAG, Vector Search, and Knowledge Graph solutions.
• Experience with Kubernetes, Docker, CI/CD, and infrastructure automation.
• Strong understanding of software design patterns, distributed architectures, and microservices.
• Experience with PostgreSQL, Redis, and NoSQL technologies.
• Knowledge of observability, monitoring, and production support processes.

About Us

At American Express, our culture is built on a 175-year history of innovation, shared values and Leadership Behaviors, and an unwavering commitment to back our customers, communities, and colleagues. From delivering differentiated products to providing world-class customer service, we operate with a strong risk mindset, ensuring we continue to uphold our brand promise of trust, security, and service.

As part of Team Amex, you'll experience our powerful backing with comprehensive support for your holistic well-being and many opportunities to learn new skills, develop as a leader, and grow your career. Here, your voice and ideas matter, your work makes an impact, and together, you will help us define the future of American Express.

About The Team

• Competitive base salaries
• Bonus incentives
• Support for financial-well-being and retirement
• Comprehensive medical, dental, vision, life insurance, and disability benefits (depending on location)
• Flexible working model with hybrid, onsite or virtual arrangements depending on role and business need
• Generous paid parental leave policies (depending on your location)
• Free access to global on-site wellness centers staffed with nurses and doctors (depending on location)
• Free and confidential counseling support through our Healthy Minds program
• Career development and training opportunities

American Express is an equal opportunity employer and makes employment decisions without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran status, disability status, age, or any other status protected by law.

Offer of employment with American Express is conditioned upon the successful completion of a background verification check, subject to applicable laws and regulations.