Company Overview

ExeQut is a fast-growing consulting firm specializing in enterprise applications, cloud solutions, AI-driven platforms, cybersecurity, and software development. We emphasize transparency, collaboration, and innovation, helping businesses optimize their technology strategies.

About the Role

ExeQut is seeking a highly experienced and technically proficient Senior AI Engineer to drive our development efforts in cutting-edge Generative AI solutions.

This role is centered on the practical architecture, deployment, and optimization of Large Language Models (LLMs), RAG systems, and AI Agents in a production environment. The ideal candidate possesses deep expertise in Python, AI orchestration frameworks, and vector database technologies, with a proven ability to translate complex AI concepts into scalable, reliable enterprise solutions.

Key Responsibilities

LLM & Agentic Systems: Architect and implement solutions leveraging LLMs, embeddings, AI agents, and advanced RAG (Retrieval-Augmented Generation) frameworks.
Prompt Engineering: Apply expert-level prompt engineering and fine-tuning techniques for LLM optimization and performance.
Language Proficiency: Utilize expert-level proficiency in Python for AI/ML development, alongside strong working knowledge of JavaScript/TypeScript for front-end integration.
Data Technologies: Implement and maintain systems utilizing vector databases (e.g., Pinecone, Weaviate, Qdrant, or similar) for semantic search and knowledge retrieval.
API Integration: Develop and integrate robust API services (RESTful, GraphQL) to expose AI functionalities across enterprise applications.

Production Experience & Deployment

AI Workflow: Architect and deploy production-grade AI workflows, semantic search architectures, and automation systems at scale.
Orchestration: Deep understanding and practical experience with AI orchestration frameworks and workflow automation tools.
Cloud Deployment: Leverage cloud platforms (AWS, Azure, or GCP) to design and deploy highly available and secure AI infrastructure using containerization (Docker).
Best Practices: Implement and enforce best practices for LLM integration, optimization, security, and monitoring.

Qualifications

Technical Expertise

AI Core: 5+ years of experience Hands-on experience with LLMs, AI agents, embeddings, RAG systems, vector databases, and prompt engineering.
Primary Language: Fluent in Python (primary) for all stages of the ML lifecycle.
Secondary Language: Strong experience with JavaScript/TypeScript (secondary) for integration work.
Tools: Experience with vector database technologies (Pinecone, Weaviate, Qdrant, or similar).
DevOps: Strong Git version control and collaborative development practices, with familiarity in Docker/containerization.
Cloud: Strong experience with cloud platforms (AWS, Azure, or GCP) for AI deployment.

Soft Skills & Professionalism

Autonomy: Self-driven, independent worker with excellent problem-solving abilities, capable of owning and executing complex feature development end-to-end.
Quality: Strong documentation practices and adherence to high code quality standards.
Communication: Advanced English writing and speaking skills (fluent communication required) to articulate technical architectures and findings clearly.
Solutioning: Proven ability to translate complex AI concepts and business needs into scalable technical solutions.

Work Schedule

This role supports a US-based client (NIH) and requires specific overlap with the US Eastern Standard Time (EST) zone.

Role Type: Full Time
Location & Time zone: Remote & Must overlap at least 3 hours with US EST.
Preferred Shift: 4:00 AM 12:00 PM EST (perfect alignment for overlapping collaboration).
Work Week: Standard work week (Monday - Friday) is preferred, but a generic "Sunday - Thursday" work week (with Friday off) is acceptable if required by your local region, provided the Sunday work is productive.

If you're a driven product

Senior AI Engineer at ExeQut