Senior AI Software Engineer

EPAM Systems; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

EPAM Systems seeks a Senior AI Software Engineer to design, build and deploy AI and ML solutions from concept to production. You will partner with cross-functional teams to deliver scalable AI products and reusable components.

You bring strong software engineering fundamentals and applied data science skills, with hands-on experience in agentic AI frameworks and cloud platforms (AWS/Azure/GCP). You will advance automation and deliver robust cloud-native services.

Qualifications

• Proven experience building and deploying AI/ML/data science solutions in production.
• Hands-on experience with agentic AI frameworks and patterns, including MCPs.
• Strong software engineering fundamentals, including web app development and API design.
• Cloud platform experience (AWS/Azure/GCP).
• Database knowledge, including data modeling and performance considerations.
• Testing capability across functional, integration and performance testing.
• Agile delivery experience and stakeholder communication.

Responsibilities

• Lead development of ML, analytics and AI solutions that solve business problems.
• Design and deliver AI products, including agentic AI applications for real use cases.
• Build and operate AI agents and supporting services from PoC through production.
• Develop Front-end and back-end software components aligned to cloud and data architecture standards.
• Improve engineering workflows through automation, reusable components and tooling.
• Partner with teams to define cloud-based application and agent architecture, design and implementation.
• Translate business requirements into technical designs using Agile delivery methods.

Skills

Applied AI

Machine Learning

Data Science

API Design

Software Engineering

Agile Delivery

Communication

Tools

AWS

Azure

GCP

Databricks

Snowflake

Dataiku

Docker

Kubernetes

CI/CD

Job description

We are seeking a Senior AI Software Engineer to build agentic AI and machine learning solutions from proof of concept to production. You will design, develop and deploy AI products, partner with cross-functional teams and improve delivery through automation and reusable components. You bring strong software engineering fundamentals plus applied data science skills.

Responsibilities

Lead development of machine learning, advanced analytics and AI solutions that solve business problems Design and deliver AI products, including agentic AI applications for real use cases Build and operate AI agents and supporting services from proof of concept through production Develop Front-end and back-end software components aligned to cloud and data architecture standards Improve engineering workflows through automation, reusable components and tooling Test solutions using functional and performance techniques to meet quality targets Partner with teams to define cloud-based application and agent architecture, design and implementation Translate business requirements into technical designs using Agile delivery methods

Requirements

Proven experience building and deploying AI, machine learning or data science solutions in production environments Hands-on experience with agentic AI frameworks and patterns, including skills, harnesses and model context protocols (MCPs) Strong software engineering fundamentals, including web application development and API design Cloud platform experience (for example, Amazon Web Services (AWS), Microsoft Azure or Google Cloud Platform (GCP)) Database knowledge, including data modeling, implementation and performance considerations Testing capability, including functional, integration and performance testing approaches Agile delivery experience, including iteration planning, delivery and continuous improvement Clear communication with the ability to explain technical tradeoffs to diverse stakeholders

Nice to have

Experience with AI and workflow platforms such as Databricks, Snowflake, Dataiku, n8n, Northflank or Microsoft Power Automate Containerization and orchestration exposure, such as Docker and Kubernetes (K8s) Experience with automated build, test and deployment pipelines, such as CI/CD practices and tooling