We are seeking a highly skilled and forward-thinking AI Engineer specialized in Large Language Models (LLMs) to design, develop, and deploy innovative AI-powered applications and intelligent agents. The ideal candidate will possess deep expertise in LLM engineering, including advanced prompt engineering strategies, fine-tuning, evaluation methodologies, and the development of systems using frameworks like Lang chain/Lang Graph. You will have a strong background in software engineering and a passion for pushing the boundaries of what's possible with generative AI, bringing solutions from ideation and research through to robust and scalable production deployment.
Experience: 8 to 10 years of overall software development experience, with at least 5+ years specifically focused on AI development, including
significant hands-on experience with Large Language Models, agent development, and related technologies.
Required Qualifications
Educational Background: Bachelor’s or master’s degree in computer science, Artificial Intelligence, Machine Learning, Computational Linguistics, or a closely related technical field.
Professional Experience: 7-8 years of progressive experience in software development, with a minimum of 5+ years dedicated to AI development, including substantial hands-on experience in designing, building, and deploying LLM-based systems and AI agents.
Programming Proficiency: Expert proficiency in Python and its ecosystem relevant to AI and LLMs.
LLM, NLP & Agent Expertise: Deep understanding of Natural Language Processing (NLP) concepts, Transformer architectures, the inner workings of Large Language Models, and principles of AI agent design.
LLM Frameworks & Tools: Significant hands-on experience with LLM-specific libraries and frameworks such as Hugging Face Transformers, Langchain, LangGraph, LlamaIndex, and similar tools for building LLM applications and agents.
Cloud Platform Experience: Solid experience with one or more major cloud platforms (AWS, GCP, Azure) and their respective AI/ML services, particularly those for deploying and managing LLMs (e.g., Amazon Bedrock, Google Vertex AI, Azure OpenAI Service).
Fine-Tuning & Evaluation Experience: Demonstrable experience in fine-tuning LLMs and implementing robust evaluation strategies for both models and agent performance.
MLOps/LLMOps Practices: Experience with MLOps principles and tools, adapted for the LLM lifecycle (e.g., experiment tracking, model registries, CI/CD for LLMs and agent-based systems).
Data Handling for LLMs: Understanding of data preprocessing, augmentation, and management techniques for training and fine-tuning LLMs.
Version Control: Proficiency with Git and collaborative development workflows.
Preferred Qualifications
Advanced LLM Architectures & Prompt Engineering: Deep experience with various LLM architectures, their trade-offs, and mastery of advanced prompt engineering techniques.
Autonomous Agent & Multi-Agent Systems: Proven experience in designing, developing, and deploying autonomous AI agents or complex multi-agent systems.
Vector Databases: Familiarity with vector databases (e.g., Pinecone, Weaviate, Milvus, Chroma) for retrieval augmented generation (RAG) and semantic search in agentic architectures.
Distributed Systems for LLMs: Knowledge of distributed training and inference techniques for very large models.
Ethical AI & Responsible LLM/Agent Development: Strong understanding of ethical considerations, bias detection, and responsible AI practices in the context of LLMs and AI agents.
Research & Publications: Contributions to LLM or AI agent research, publications in relevant conferences/journals, or active participation in open-source LLM/agent projects.
Domain-Specific LLM/Agent Applications: Experience applying LLMs and agents to solve problems in specific industry domains.
Cloud Certifications: Relevant cloud certifications (e.g., AWS Certified Machine Learning, Google Professional Machine Learning Engineer, Microsoft Certified: Azure AI Engineer Associate or similar MCP credentials).
What We Offer
• Competitive compensation and benefits package.
• Opportunities to work on both legacy modernization and cutting-edge projects.
• Collaborative and growth-oriented work environment.
