AWS AI/ML Solutions Engineer (Client Technical Engineer)

Lean Solutions Group; Philippines; On-site; PHP 1,500,000 - 3,200,000; Full time

Job summary

Lean Solutions Group is seeking a client-facing AWS AI/ML Solutions Architect to connect cloud AI innovations with enterprise goals. You will lead discovery, translate business needs into secure cloud architectures, and deploy end-to-end AI/ML systems for client environments.

You will design scalable ML architectures, implement AWS AI platforms, and build streaming pipelines with MLOps practices. Strong Python and IaC skills, plus experience with CI/CD for ML, are essential.

Qualifications

• 2-3 years of direct experience architecting and deploying AI/ML systems within the AWS ecosystem.
• Deep functional expertise in the AWS Machine Learning suite, particularly SageMaker, Bedrock, and foundational services.
• Hands-on familiarity with Generative AI frameworks, NLP, and Computer Vision.
• Strong programming proficiency in Python with data science libraries (TensorFlow, PyTorch, Pandas, Scikit-learn).
• Proven background in Infrastructure as Code tools like AWS CloudFormation or Terraform.
• Demonstrated success establishing automated CI/CD for machine learning models.
• Experience designing secure RESTful/cloud API integrations into existing corporate environments.

Responsibilities

• Lead client discovery workshops, serving as the technical authority to align business demands with AWS AI/ML service capabilities.
• Design scalable, secure, and cost-optimized ML architectures tailored to customer infrastructure.
• Present technical blueprints, proofs-of-concept, and implementation roadmaps to leadership and teams.
• Implement managed AWS AI platforms, including Bedrock, Lex, Comprehend, Rekognition, Textract, and Kendra.
• Build, train, and deploy custom predictive models via SageMaker when off-the-shelf services need enhancement.
• Develop prompt engineering tactics and fine-tune LLMs on client datasets.
• Construct secure API pipelines connecting AWS cognitive modules to legacy systems.
• Establish MLOps standards and CI/CD workflows to track model performance and deployment.
• Troubleshoot performance bottlenecks, latency, and output inaccuracies during rollouts.
• Conduct post-launch performance reviews to refine API consumption and reduce AWS costs.

Skills

AWS AI/ML

SageMaker

Bedrock

NLP

Computer Vision

Python

IaC

CI/CD

REST APIs

Tools

AWS CloudFormation

Terraform

Git

Job description

Join Our Team at Lean Solutions Group (LSG)!

Lean Solutions Group (LSG) is a next-generation solutions provider combining AI-driven automation, industry expertise, and tech-powered talent. Built in the demanding Supply Chain sector, our model now supports 600+ clients across multiple industries, powered by 10,000+ employees in five countries. We help businesses achieve immediate efficiency, long-term resilience, and scalable growth by integrating intelligent technology, optimized processes, and high-performance teams.

At LSG, we believe in your talent and your potential. Join a multicultural, people-first environment where you can grow, sharpen your skills, and unlock new career opportunities. Here, every day brings fresh challenges, collaboration, and purpose.

Our Mission:

Transform business challenges into lasting success through purpose-built teams, technology, and expertise.

Our Vision:

A world where people, empowered by technology, turn any challenge into a catalyst for growth.

About the Role

Global Technology Services is seeking a client-facing AWS AI/ML Solutions Architect to serve as the key technical expert connecting cutting-edge cloud AI innovations with enterprise client goals. In this role, you will lead discovery engagements, translate business requirements into secure cloud architectures, and deploy end-to-end artificial intelligence and machine learning systems within client environments.

Key Responsibilities

• Lead client discovery workshops, serving as the technical authority to align business demands with AWS AI/ML service capabilities.
• Design scalable, secure, and cost-optimized machine learning architectures tailored to customer infrastructure.
• Present technical blueprints, proofs-of-concept, and implementation roadmaps to executive leadership and technical teams.
• Implement managed AWS AI platforms, including Amazon Bedrock, Lex, Comprehend, Rekognition, Textract, and Kendra.
• Build, train, and deploy custom predictive models via Amazon SageMaker when out-of-the-box services require enhancement.
• Develop prompt engineering tactics and fine-tune Large Language Models (LLMs) on client-specific datasets.
• Construct secure API pipelines connecting AWS cognitive modules to legacy enterprise systems.
• Establish MLOps standards and CI/CD workflows to track model performance, automate deployment, and prevent production drift.
• Troubleshoot performance bottlenecks, latency issues, and output inaccuracies during pre-production and live rollouts.
• Conduct post-launch performance reviews to refine API consumption and lower overall AWS infrastructure costs.

Qualifications & Core Skills

• 2-3 years of direct experience architecting and deploying AI/ML systems within the AWS ecosystem.
• Deep functional expertise in the AWS Machine Learning suite, particularly SageMaker, Bedrock, and foundational services.
• Hands-on familiarity with Generative AI frameworks, Natural Language Processing (NLP), and Computer Vision.
• Strong programming proficiency in Python alongside data science libraries (TensorFlow, PyTorch, Pandas, Scikit-learn).
• Proven background in Infrastructure as Code (IaC) tools like AWS CloudFormation or Terraform.
• Demonstrated success establishing automated CI/CD automation for machine learning models.
• Experience designing secure RESTful/cloud API integrations into existing corporate environments.

Preferred Qualifications

• AWS Certified Machine Learning - Specialty
• AWS Certified Solutions Architect

Professional Attributes

• Assertive, consultative communication style with strong client-facing authority.
• Proven capability to manage client expectations while simplifying complex AI concepts for business stakeholders.
• Analytical mindset focused on system reliability, performance tuning, and business value delivery.