AI Engineer

Description & Requirements
Position Summary

The AI Engineer with GenAI expertise is responsible for developing advanced technical solutions, integrating cutting-edge generative AI technologies. This role requires a deep understanding of modern technical and cloud-native practices, AI, DevOps, and machine learning technologies, particularly in generative models. You will support a wide range of customers through the “Ideation to MVP” journey, demonstrating proficiency in leading projects and ensuring delivery excellence.

Key Responsibilities

Technical & Engineering Leadership

• Develop solutions leveraging GenAI technologies, integrating advanced AI capabilities into cloud-native architectures to enhance system functionality and scalability.

• Lead the design and implementation of GenAI-driven applications, ensuring seamless integration with microservices and container-based environments.

• Create solutions that fully leverage the capabilities of modern microservice and container-based

environments running in public, private, and hybrid clouds.

• Contribute to thought leadership across the Cloud Native domain with an expert understanding of open-source technologies (e.g., Kubernetes/CNCF) and partner technologies.

• Collaborate on joint technical projects with partners, including Google, Microsoft, AWS, IBM, Red Hat, Intel, Cisco, and Dell/VMware.

Service Delivery

• Engineer innovative GenAI solutions from ideation to MVP, ensuring high performance and reliability

within cloud-native frameworks.

• Optimize AI models for deployment in cloud environments, balancing efficiency and effectiveness to meet client requirements and industry standards.

• Assess existing complex solutions and recommend appropriate technical treatments to transform

applications with cloud-native/12-factor characteristics.

• Refactor existing solutions to implement a microservices-based architecture.

Innovation & Initiative

• Drive the adoption of cutting-edge GenAI technologies within cloud-native projects, spearheading

initiatives that push the boundaries of AI integration in cloud services.

• Engage in technical innovation and support position as an industry leader.

• Author whitepapers, blogs, and speak at industry events.

• Maintain hands-on technical credibility, stay ahead of industry trends, and mentor others.

Client Relationships

• Provide expert guidance to clients on incorporating GenAI and machine learning into their cloud-native systems, ensuring best practices and strategic alignment with business goals.

• Conduct workshops and briefings to educate clients on the benefits and applications of GenAI, establishing strong, trust-based relationships.

• Perform a trusted advisor role, contributing to technical projects (PoCs and MVPs) with a strong focus on technical excellence and on-time delivery.

Mandatory Skills & Experience

• A passionate developer with 7+ years of experience in Java, Python, and Kubernetes, comfortable working as part of a paired/balanced team.

• Extensive experience in software development, with significant exposure to AI/ML technologies.

• Expertise in GenAI frameworks: Proficient in using GenAI frameworks and libraries such as LangChain, OpenAI API, and Hugging Face Transformers.

• Prompt engineering: Experience in designing and optimizing prompts for various AI models to achieve desired outputs and improve model performance.

• Strong understanding of NLP techniques and tools, including tokenization, embeddings, transformers, and language models.

• Proven experience developing complex solutions that leverage cloud-native technologies—featuring

container-based, microservices-based approaches; based on applying 12-factor principles to application engineering.

• Exemplary verbal and written communication skills (English).

• Positive and solution-oriented mindset.

• Solid experience delivering Agile and Scrum projects in a Jira-based project management environment.

• Proven leadership skills and the ability to lead projects to ensure delivery excellence

Desired Skills & Experience

• Machine Learning Operations (MLOps): Experience in deploying, monitoring, and maintaining AI models in production environments using MLOps practices.

• Data engineering for AI: Skilled in data preprocessing, feature engineering, and creating pipelines to feed AI models with high-quality data.

• AI model fine-tuning: Proficiency in fine-tuning pre-trained models on specific datasets to improve

performance for specialized tasks.

• AI ethics and bias mitigation: Knowledgeable about ethical considerations in AI and experienced in

implementing strategies to mitigate bias in AI models.

• Knowledgeable about vector databases, LLMs, and SMLs, and integrating with such models.

• Proficient with Kubernetes and other cloud-native technologies, including experience with commercial Kubernetes distributions (e.g., Red Hat OpenShift, VMware Tanzu, Google Anthos, Azure AKS, Amazon EKS, Google GKE).

• Deep understanding of core practices including DevOps, SRE, Agile, Scrum, Domain-Driven Design, and familiarity with the CNCF open-source community.

• Recognized with multiple cloud and technical certifications at a professional level, ideally including AI/ML specializations from providers like Google, Microsoft, AWS, Linux Foundation, IBM, or Red Hat.

Verifiable Certification

• At least one recognized cloud professional / developer certification (AWS/Google/Microsoft)

2.

SKILLS

Agile Scrum

API

Azure

C++

Data Engineering

FEED

Java

Kubernetes

Linux

Machine Learning

Pipelines

Python

PyTorch

TensorFlow

Tokenization