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.
