Lead AI Engineer – AI Foundations, LLM Core & Agentic AI

Role Summary

We are seeking a Lead AI Engineer to drive the design and development of advanced AI systems, with a focus on foundational models, Large Language Models (LLMs), and agentic AI frameworks. This role will lead the architecture and deployment of scalable, production-grade AI solutions that power intelligent automation and next-generation applications.

Key Responsibilities

• Lead the design and development of AI/ML systems with a focus on LLMs and agent-based architectures.
• Build and optimize pipelines for training, fine-tuning, and deploying large language models.
• Architect AI platforms supporting retrieval-augmented generation (RAG), embeddings, and vector databases.
• Develop and manage agentic workflows, orchestration layers, and multi-agent systems.
• Integrate AI capabilities into enterprise applications via APIs and microservices.
• Ensure scalability, performance, and reliability of AI systems in production.
• Collaborate with data scientists, software engineers, and product teams to deliver AI-driven solutions.
• Establish best practices for prompt engineering, model evaluation, and responsible AI.
• Implement monitoring, logging, and continuous improvement for deployed AI systems.
• Stay up to date with advancements in generative AI, LLM frameworks, and tooling.

Required Skills & Qualifications

• Bachelor’s or Master’s degree in Computer Science, AI, Machine Learning, or related field.
• 7–10+ years of experience in software engineering/AI engineering, with strong hands-on expertise in ML systems.
• Deep understanding of:
  • Large Language Models (LLMs) and transformer architectures
  • NLP, embeddings, and semantic search
  • Retrieval-Augmented Generation (RAG)
• Strong programming skills in Python (preferred) and experience with ML frameworks (PyTorch, TensorFlow).
• Experience with LLM ecosystems and tools (e.g., LangChain, LlamaIndex, vector DBs like Pinecone, FAISS).
• Knowledge of API design, microservices, and distributed systems.
• Experience with cloud platforms (Azure, AWS, or GCP) and MLOps practices.

Preferred Qualifications

• Experience working with open-source and proprietary LLMs (e.g., GPT, LLaMA, etc.).
• Exposure to agentic AI frameworks and autonomous systems.
• Knowledge of fine-tuning techniques (LoRA, RLHF, etc.).
• Experience with Kubernetes, Docker, and scalable deployment architectures.
• Familiarity with AI safety, governance, and ethical AI principles.

Key Competencies

• Technical leadership and architecture design
• Innovation and problem-solving mindset
• Strong collaboration and communication skills
• Ability to translate business problems into AI solutions
• Mentorship and team development

Nice to Have

• Experience building AI platforms or internal AI tooling
• Contributions to open-source AI projects
• Background in data engineering or big data technologies