Job description

Description Note: GlobalLogic estimates the starting pay range for this role to be performed hybrid in Reading, PA is $135,000 – $145,000 and reflects base salary only. This pay range is provided as a good faith estimate and the amount offered may be higher or lower. GlobalLogic takes many factors into consideration in making an offer, including candidate qualifications, work experience, operational needs, travel and onsite requirements, internal peer equity, prevailing wage, responsibilities, and other market and business considerations. Job Title: Senior AWS AgentCore Platform Engineer Location: Reading, PA (Hybrid) Work with Client team to harden the new AWS AgentCore platform being setup so it can be used by teams to develop and deployAI agents. Requirements • Python (3.11+): Hands-on experience building robust applications and agentic logic. • Amazon SageMaker Unified Studio (SMUS) & MLOps: Hands-on experience with SMUS to manage the end-to-end ML lifecycle. This includes automating model training, registering models in the centralized registry, and configuring real-time/asynchronous inference endpoints within the SMUS ecosystem. • Infrastructure as Code (IaC):Production-level expertise using Terraform to provision AWS services, including Bedrock Agents, Knowledge Bases, DynamoDB, Lambda, etc. • CI/CD & DevOps: Proven track record in building and maintaining automated deployment pipelines for cloud-native applications and MLOps workflows. • AWS Security & Identity: Deep experience with IAM for fine-grained permissions and secure service communication. • AWS Bedrock AgentCore : Familiarity with the latest ecosystem, specifically the Agent Runtime, Memory, Identity, and Tool Gateway layers. • Multi-Agent Orchestration: Experience building or enabling collaborative systems using frameworks like LangGraph, strands, etc. • Model Context Protocol (MCP): Strong understanding of MCP and modern tool/API integration patterns for AI agents. • AWS Certification (Optional): Holding one or more AWS Professional-level certifications—specifically the AWS Certified AI Practitioner or AWS Certified Machine Learning – Specialty—is highly preferred and considered a strong plus. • Continuous Learning: Willingness to learn emerging AI technologies, frameworks, and evolving cloud services. Job responsibilities • Python (3.11+): Hands-on experience building robust applications and agentic logic. • Amazon SageMaker Unified Studio (SMUS) & MLOps: Hands-on experience with SMUS to manage the end-to-end ML lifecycle. This includes automating model training, registering models in the centralized registry, and configuring real-time/asynchronous inference endpoints within the SMUS ecosystem. • Infrastructure as Code (IaC):Production-level expertise using Terraform to provision AWS services, including Bedrock Agents, Knowledge Bases, DynamoDB, Lambda, etc. • CI/CD & DevOps: Proven track record in building and maintaining automated deployment pipelines for cloud-native applications and MLOps workflows. • AWS Security & Identity: Deep experience with IAM for fine-grained permissions and secure service communication. • AWS Bedrock AgentCore : Familiarity with the latest ecosystem, specifically the Agent Runtime, Memory, Identity, and Tool Gateway layers. • Multi-Agent Orchestration: Experience building or enabling collaborative systems using frameworks like LangGraph, strands, etc. • Model Context Protocol (MCP): Strong understanding of MCP and modern tool/API integration patterns for AI agents. • AWS Certification (Optional): Holding one or more AWS Professional-level certifications—specifically the AWS Certified AI Practitioner or AWS Certified Machine Learning – Specialty—is highly preferred and considered a strong plus. • Continuous Learning: Willingness to learn emerging AI technologies, frameworks, and evolving cloud services. What we offer Exciting Projects: Come take your place at the forefront of digital transformation! With clients across all industries and sectors, we offer an opportunity to work on market-defining products using the latest technologies. Collaborative Environment: You can expand your skills by collaborating with a diverse team of highly talented people in an open, laidback environment — or even abroad in one of our global centers or client facilities! Work-Life Balance: GlobalLogic prioritizes work-life balance, which is why we offer flexible work schedules and opportunities to work from home. Professional Development: We provide continuing education classes, professional certification and training (technical, soft skills, language, and communication skills) to help you realize your professional goals. Being part of a global organization, there are additional learning opportunities through international knowledge exchanges. Excellent Benefits: We provide our employees with competitive salaries, health and life insurance, short-term and long-term disability insurance, a matched contribution 401K plan, flexible spending accounts, and PTO and holidays About GlobalLogic GlobalLogic, a Hitachi Group Company, is a trusted digital engineering partner to the world’s largest and most forward-thinking companies. Since 2000, we’ve been at the forefront of the digital revolution – helping create some of the most innovative and widely used digital products and experiences. Today we continue to collaborate with clients in transforming businesses and redefining industries through intelligent products, platforms, and services.

Requirements

Must-haves

Job Title: Senior AWS AgentCore Platform Engineer
Location: Reading, PA (Hybrid)
Work with Client team to harden the new AWS AgentCore platform being setup so it can be used by teams to develop and deployAI agents.
Python (3.11+): Hands-on experience building robust applications and agentic logic.
Infrastructure as Code (IaC):Production-level expertise using Terraform to provision AWS services, including Bedrock Agents, Knowledge Bases, DynamoDB, Lambda, etc.
CI/CD & DevOps: Proven track record in building and maintaining automated deployment pipelines for cloud-native applications and MLOps workflows.
AWS Security & Identity: Deep experience with IAM for fine-grained permissions and secure service communication.
AWS Bedrock AgentCore : Familiarity with the latest ecosystem, specifically the Agent Runtime, Memory, Identity, and Tool Gateway layers.
Multi-Agent Orchestration: Experience building or enabling collaborative systems using frameworks like LangGraph, strands, etc.
Model Context Protocol (MCP): Strong understanding of MCP and modern tool/API integration patterns for AI agents.
Continuous Learning: Willingness to learn emerging AI technologies, frameworks, and evolving cloud services.
Python (3.11+): Hands-on experience building robust applications and agentic logic.
Infrastructure as Code (IaC):Production-level expertise using Terraform to provision AWS services, including Bedrock Agents, Knowledge Bases, DynamoDB, Lambda, etc.
CI/CD & DevOps: Proven track record in building and maintaining automated deployment pipelines for cloud-native applications and MLOps workflows.
AWS Security & Identity: Deep experience with IAM for fine-grained permissions and secure service communication.
AWS Bedrock AgentCore : Familiarity with the latest ecosystem, specifically the Agent Runtime, Memory, Identity, and Tool Gateway layers.
Multi-Agent Orchestration: Experience building or enabling collaborative systems using frameworks like LangGraph, strands, etc.
Model Context Protocol (MCP): Strong understanding of MCP and modern tool/API integration patterns for AI agents.
Continuous Learning: Willingness to learn emerging AI technologies, frameworks, and evolving cloud services.

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Keywords to mirror

awspythonmlci/cdapiaimachine learning

Platform: globallogic
Effort: medium

Benefits

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Vision

401k

Pto

Professional Development