Offer summary

Qualifications:

Bachelor's or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field., 6+ years of hands-on experience in AI/ML, focusing on Generative AI and LLM-based architectures., Expertise in architecting scalable AI/ML solutions and strong knowledge of AI deployments on cloud platforms., Relevant certifications in AI/ML or cloud computing are a plus..

Key responsabilities:

Design end-to-end architectures for AI and machine learning solutions tailored to business needs.

Develop scalable frameworks for AI models and implement document retrieval and semantic search.

Collaborate with cross-functional teams to integrate AI capabilities into products and services.

Mentor junior engineers and ensure adherence to best practices in AI projects.

Job description

Position Type : Part-Time

Location : Remote

Working Hours: 4-5 Hours a day - Monday - Friday - during 10 AM - 7 PM

Job Description:

We are looking for an experienced Senior Generative AI Architect with 6+ years of experience to lead the design, development, and deployment of cutting-edge AI solutions. The ideal candidate will have deep expertise in Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and AI/ML architecture. This role requires strategic thinking, hands-on implementation skills, and the ability to collaborate with cross-functional teams to drive AI innovation.

Key Responsibilities:

Architect and Lead AI Solutions: Design end-to-end architectures for AI and machine learning solutions tailored to business needs.
AI Model Development: Develop scalable and maintainable frameworks for AI models, pipelines, and systems.
Vector Search & Data Retrieval: Implement and optimize document retrieval, semantic search, and contextual query answering using VectorDBs (e.g., Pinecone, Weaviate, FAISS).
Prompt Engineering & Optimization: Design and refine advanced prompting techniques to enhance AI model efficiency.
LangGraph & GenAI Frameworks: Utilize LangGraph, LangChain, HuggingFace, OpenAI APIs, and other AI frameworks for efficient workflow orchestration.
MLOps & AI Deployment: Architect systems for deploying AI models into production (e.g., using Docker, Kubernetes, or serverless technologies) and ensure CI/CD pipelines for automated testing and deployment.
AI Infrastructure & Cloud Integration: Define technology stacks, tools, and infrastructure for AI solutions, including cloud-based and on-premise systems.
Scalability & Performance: Ensure solutions are scalable and capable of handling high throughput and low-latency requirements.
Data Engineering Collaboration: Work closely with data engineers to design robust data pipelines for training and inference.
AI Ethics & Governance: Ensure AI solutions adhere to responsible AI principles, compliance, and security best practices.
Cross-functional Collaboration: Work closely with data scientists, engineers, and business stakeholders to integrate AI capabilities into products and services.
Continuous Innovation: Explore new tools and techniques to improve existing systems, prototype innovative solutions, and assess their feasibility for production.
Technical Leadership & Mentorship: Mentor junior engineers, data scientists, and developers in best practices, establishing coding standards, design principles, and operational guidelines for AI projects.
Code Review & Best Practices: Review code to ensure it follows best practices and is performant.
Technical Communication: Effectively communicate technical concepts to non-technical stakeholders.
Stay Updated: Keep up with the latest trends, frameworks, and breakthroughs in AI and machine learning.

Required Skills:

AI/ML Architecture: Expertise in architecting scalable AI/ML solutions, focusing on Generative AI and LLMs.
LLM Fine-Tuning & Optimization: Strong experience in pre-training, fine-tuning, and optimizing large-scale AI models.
Vector Databases: Proficiency with Pinecone, Weaviate, FAISS, or similar for efficient document storage and retrieval.
GenAI Frameworks: Hands-on experience with LangGraph, LangChain, HuggingFace, OpenAI API, and other Generative AI tools.
Cloud & DevOps: Strong knowledge of AI deployments on AWS, GCP, or Azure using Kubernetes, Docker, and Terraform.
NLP & RAG Techniques: Expertise in transformer models, embeddings, tokenization, and retrieval-augmented generation workflows.
Prompt Engineering: Deep understanding of prompt crafting, tuning, and optimization for LLM performance enhancement.
MLOps & CI/CD: Experience in ML model lifecycle management, version control, and automated deployment.
Problem-Solving & Leadership: Strong analytical skills with the ability to solve complex AI challenges and mentor teams.

Preferred Skills:

Experience in multi-modal AI (text, vision, audio integration).
Knowledge of ethical AI frameworks and AI governance best practices.
Familiarity with real-time AI inference optimization techniques.
Hands-on experience with distributed AI computing and GPU acceleration.
Understanding of data privacy laws and AI compliance regulations.

Qualifications:

Education: Bachelor's or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field.
Experience: 6+ years of hands-on experience in AI/ML, with a focus on Generative AI and LLM-based architectures.
Certifications: Relevant certifications in AI/ML, cloud computing (AWS, GCP, Azure), or MLOps are a plus.
Publications & Contributions: Contributions to AI research, open-source projects, or patents in AI/ML are highly desirable.

Required profile