The Senior AI Engineer will architect and implement solutions leveraging LLMs, embeddings, AI agents, and advanced RAG frameworks, focusing on production deployment and optimization. This includes applying expert prompt engineering and fine-tuning techniques while developing robust API services for enterprise integration.
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 leader ready to shape powerful solutions and thrive in a fast-paced, collaborative environment we'd love to see your application!