Senior AI Solutions Engineer (AWS)

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.

Recommended Job Title Options

The original title "Client Technical Engineer" is overly broad and masks the AI/AWS focus. Using modern, search-indexed terminology attracts candidates with higher domain authority:

• Primary Title: Senior AI Solutions Engineer (AWS)
• Alternative 1: Generative AI & AWS Cloud Solutions Architect
• Alternative 2: Client Solutions Engineer – Applied AI/ML (AWS)

Optimized Job Description

Senior AI Solutions Engineer (AWS)

About the Role We are seeking a Senior AI Solutions Engineer to bridge the gap between advanced cognitive computing capabilities and client business objectives. In this high-impact, client-facing role, you will lead technical discovery, architect scalable AI-driven solutions within the AWS ecosystem, and drive hands-on deployment through post-launch optimization.

The ideal candidate combines deep Python and AWS AI/ML technical expertise with a consultative communication style capable of building trust with engineering teams and executive stakeholders alike.

Key Responsibilities

1. Client Engagement & Solution Architecture

• Technical Authority: Lead client discovery workshops to assess technical environments and translate business challenges into tailored AWS AI/ML architectures.
• Architecture & Strategy: Design resilient, production-ready AI solutions prioritizing security, governance, low-latency performance, and cloud cost efficiency.
• Executive Presentation: Build and present proof-of-concepts (POCs), technical roadmaps, and trade-off analyses to technical teams and C-suite executives.
• Expectation Alignment: Manage project scope strictly to ensure technical deliverables directly align with measurable client business outcomes.

2. AI/ML Engineering & AWS Implementation

• AWS AI Deployment: Integrate managed AWS AI services (Amazon Bedrock, Lex, Comprehend, Rekognition, Textract, Kendra) into enterprise application pipelines.
• Custom Models & GenAI: Leverage Amazon SageMaker to build, fine-tune, and deploy custom models when managed services fall short.
• LLM Engineering: Execute prompt engineering, Retrieval-Augmented Generation (RAG), and fine-tuning strategies on LLMs deployed via AWS Bedrock.
• API Integration: Design secure, high-throughput REST APIs and microservices to integrate cloud cognitive services with client enterprise infrastructure.

3. MLOps, Optimization & Technical Support

• Pipeline Automation: Establish CI/CD and MLOps pipelines to automate model testing, deployment, and real-time monitoring for model drift.
• Production Troubleshooting: Diagnose and resolve latency bottlenecks, API failure rates, and model performance degradation across staging and production.
• Cost & Performance Audits: Conduct post-deployment optimizations to maximize API efficiency and reduce overall AWS compute spend.

Qualifications & Experience

Core Requirements

• Experience: 3+ years of hands-on experience in AI/ML solution deployment and engineering within the AWS cloud ecosystem.
• AWS ML Ecosystem: Direct production experience with Amazon Bedrock, SageMaker, core AWS AI services, and integration points like AWS Lambda and API Gateway.
• Programming Languages: Advanced Python proficiency alongside key data science frameworks (PyTorch, TensorFlow, Pandas, Scikit-learn).
• Generative AI & Architecture: Hands-on experience building GenAI workflows, including LLM orchestration tools (LangChain, LlamaIndex), vector databases, and RAG architectures.
• Infrastructure & MLOps: Practical experience with Infrastructure as Code (Terraform or AWS CloudFormation) and building CI/CD pipelines for ML models.
• Client & Consultative Skills: Proven track record translating complex technical AI concepts into clear business value for executive clients and stakeholders.

Preferred Qualifications

• AWS Certified Machine Learning – Specialty
• AWS Certified Solutions Architect – Associate or Professional