Client Technical Engineer

Lean Solutions Group; Philippines; On-site; PHP 900,000 - 1,500,000; Full time

Job summary

Lean Solutions Group seeks a Client Technical Engineer to bridge cognitive computing and client objectives within the AWS ecosystem. You will lead discovery, design production-grade AI solutions, and drive deployment with a focus on security, performance, and cost efficiency.

Ideal candidates bring 2–3 years of AWS ML experience, strong Python, and hands-on AI/ML deployment skills, with clear client-facing communication to tie technology to business outcomes.

Qualifications

• 2 to 3 years hands-on experience in cognitive engineering, ML, or AI solution deployment in AWS.
• Deep practical knowledge of AWS ML stack (SageMaker, Bedrock, core AI services).
• Proficiency in Python and data science libraries.

Responsibilities

• Lead client engagements, map business requirements to AWS AI/ML services.
• Design scalable cognitive architectures aligned with security and cost efficiency.
• Deliver POCs and architectural roadmaps to technical and executive stakeholders.
• Manage client expectations and align deliverables with business outcomes.

Skills

Python programming

AI/ML fundamentals

NLP pipelines

Data analysis

Tools

SageMaker

Bedrock

TensorFlow

PyTorch

Pandas

Scikit-learn

Terraform

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.

Role Overview:

We are seeking a Client Technical Engineer to bridge the gap between advanced cognitive computing capabilities and direct client business objectives. Operating in a high-impact, client-facing capacity, you will translate complex business problems into scalable, AI-driven architectures within the AWS ecosystem.

In this role, you will lead technical discovery, design production-grade cognitive solutions, and drive hands‑on deployment through post-launch optimization. The ideal candidate pairs strong Python technical depth with a direct, consultative communication style capable of instilling confidence in technical teams and executive stakeholders alike.

Key Responsibilities

Client Engagement & Solution Architecture

• Serve as the primary technical authority during client engagements, leading discovery sessions to map business requirements to appropriate AWS AI/ML services.

• Design robust, scalable cognitive architectures tailored to client infrastructure, prioritizing security, compliance, performance, and cost-efficiency.

• Present technical solutions, proofs-of-concept (POCs), and architectural roadmaps to both technical teams and C-suite/executive stakeholders.

• Manage client expectations strictly and align technical deliverables directly with business outcomes.

AI/ML Engineering & Implementation

• Deploy and integrate AWS managed AI services—including Amazon Bedrock, Lex, Comprehend, Rekognition, Textract, and Kendra—into client applications.

• Utilize Amazon SageMaker to build, train, tune, and deploy custom machine learning models when managed services fall short of specific use cases.

• Implement prompt engineering and fine-tuning strategies for Large Language Models (LLMs) deployed through AWS Bedrock to align with client datasets.

• Design secure API integrations connecting AWS cognitive services to existing client enterprise systems.

MLOps, Optimization & Support

• Establish CI/CD pipelines for machine learning models to automate testing, deployment, and continuous monitoring of model drift in production environments.

• Diagnose and resolve complex integration bottlenecks, latency issues, and model inaccuracies during staging and production phases.

• Conduct post-deployment audits to optimize API call efficiency and minimize overall AWS computing expenditure.

Required Skills & Qualifications

• Experience: 2 to 3 years of hands‑on experience in cognitive engineering, machine learning, or AI solution deployment, specifically within the AWS cloud environment.

• AWS ML Stack: Deep practical knowledge of the AWS Machine Learning ecosystem, including SageMaker, Bedrock, and core AI services.

• Programming & Libraries: Strong proficiency in Python and familiarity with standard data science/AI libraries (TensorFlow, PyTorch, Pandas, Scikit-learn).

• AI Domain Expertise: Hands‑on experience working with Generative AI frameworks, NLP pipelines, and computer vision technologies.

• Infrastructure & Integration: Practical knowledge of Infrastructure as Code (AWS CloudFormation or Terraform) and experience building secure enterprise API integrations.

• MLOps: Proven background establishing or supporting CI/CD pipelines for machine learning models.

• Troubleshooting: Analytical approach to identifying and resolving performance, latency, and integration bottlenecks in staging or production.

Key Competencies & Soft Skills

• Consultative Leadership: Direct, authoritative, and consultative communication style with an ability to set firm, clear expectations with clients.

• Value Translation: Proven capability to translate abstract AI/ML concepts into clear, concrete business value.

• Executive Presence: Confident during discovery sessions and formal architectural presentations to executive stakeholders.

• Problem-Solving: Systematic, analytical approach to technical troubleshooting, optimization, and solution reliability.

Nice-to-Have Qualifications

• AWS Certified Machine Learning – Specialty

• AWS Certified Solutions Architect – Associate or Professional

Join the Lean Solutions Group! Innovate, grow your career, and make a real impact with a fast-paced, collaborative global team.