Lead/Principal AI Engineer

Role Overview

Partex.AI is seeking a seasoned Lead/Principal AI Engineer to design, develop, and scale robust enterprise AI and large language model (LLM) products. The ideal candidate will possess deep expertise in Agentic AI, Retrieval-Augmented Generation (RAG), LLM orchestration, cloud AI platforms, and production-grade AI solutions.

Key Responsibilities

• Design and implement enterprise-grade LLM and AI solutions on cloud platforms such as AWS Bedrock and Azure OpenAI.
• Develop Agentic AI and multi-agent systems utilizing frameworks like LangGraph or LangChain.
• Create scalable RAG infrastructures, embedding pipelines, hybrid search capabilities, grounding mechanisms, and tool integration systems.
• Convert AI prototypes into secure, scalable, and highly available production software.
• Establish frameworks for LLM evaluation, monitoring, observability, governance, and safety standards.
• Promote best practices in AI engineering and support AI-enhanced software development.
• Provide mentorship to engineers and collaborate closely with Product, Data Science, and Engineering teams.
• Lead the technical architecture and oversee execution of strategic AI projects.

Candidate Requirements

• Minimum 8 years of experience in Software Engineering, AI/ML engineering, or Technical Architecture roles.
• Demonstrated hands-on experience developing and deploying production LLM and generative AI applications.
• Expert proficiency in Python programming, API development, microservice architectures, system design, and production-grade engineering.
• Strong background in Agentic AI, multi-agent orchestration, RAG, vector database utilization, and LLM evaluation techniques.
• Experience with cloud services such as AWS and Azure, integrating enterprise data, and managing scalable AI infrastructures.
• Proven track record of progressing AI solutions from proof-of-concept through to large scale production deployment.
• Experience leading teams and mentoring engineers, along with managing cross-functional technical initiatives.
• Familiarity with classical machine learning methodologies.

Additional Skills (Preferred)

• Knowledge of vector databases like Pinecone, Qdrant, Milvus, as well as knowledge graph and GraphRAG technologies.
• Experience with multimodal AI, fine-tuning and parameter-efficient fine-tuning (PEFT) techniques, Kubernetes, and GPU infrastructure.
• Exposure to observability and governance tools such as LangSmith and Arize Phoenix.
• Contributions to open-source projects, patents, or AI research publications are advantageous.

Ideal Profile

A practical AI technical leader capable of architecting, building, deploying, and scaling production-level LLM and Agentic AI systems.