AI Engineer

Position Overview We are seeking an innovative and technically strong AI Engineer to architect, develop, and deploy next-generation AI solutions that enhance business operations and customer experiences. This role will focus on building enterprise-scale Generative AI applications utilizing Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Model Context Protocol (MCP) services, and Agentic AI frameworks. The ideal candidate will have hands-on experience designing intelligent systems using modern AI technologies, including LLM orchestration frameworks such as LangChain and LangGraph, prompt engineering, AI agents, and production-ready AI platforms. Strong Python development skills and experience delivering scalable AI solutions are essential. You will collaborate with business stakeholders, engineering teams, and data professionals to create AI-driven capabilities that improve efficiency, automate complex workflows, and accelerate innovation across the organization. Key Responsibilities Architect and develop enterprise-grade applications powered by Large Language Models (LLMs) for internal operations, customer engagement, and productivity enhancement. Design and implement intelligent AI agents and Agentic AI ecosystems capable of planning, reasoning, and executing complex multi-step processes. Build and maintain Retrieval-Augmented Generation (RAG) solutions that leverage proprietary datasets, enterprise knowledge repositories, and structured/unstructured information sources. Develop and manage MCP (Model Context Protocol) services, multimodal AI platforms, and conversational assistants supporting both internal users and external customers. Integrate AI solutions with enterprise applications, APIs, databases, and business systems. Create effective prompt engineering strategies, evaluation methodologies, governance controls, and safety mechanisms to ensure reliable and compliant AI outputs. Improve model performance through prompt optimization, model orchestration, fine-tuning techniques, and experimentation. Establish and maintain MLOps/LLMOps workflows for deployment, monitoring, evaluation, and continuous optimization of AI applications. Participate in technology assessments and support build-versus-buy decisions while collaborating with vendors, consultants, and internal development teams. Continuously evaluate emerging trends, frameworks, tools, and innovations within Generative AI, Agentic AI, and machine learning ecosystems. Required Qualifications Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Applied Mathematics, or a related technical discipline. Minimum 2 years of professional software engineering experience with exposure to AI, machine learning, or advanced analytics solutions. Demonstrated experience building and deploying production-ready applications utilizing Large Language Models. Strong proficiency in Python and commonly used AI/ML libraries such as NumPy, Pandas, SciPy, and Scikit-learn. Hands-on experience with modern LLM ecosystems and platforms, including: OpenAI, Anthropic, Google AI, and open-source language models Hugging Face ecosystem LangChain, LlamaIndex, or equivalent AI orchestration frameworks Experience developing Retrieval-Augmented Generation (RAG) architectures and integrating vector databases. Strong understanding of embeddings, prompt design, model evaluation, and LLM optimization techniques. Experience developing APIs, backend services, MCP implementations, and scalable distributed applications. Familiarity with AI-assisted development tools such as Claude Code, Codex, Cursor, GitHub Copilot, or similar platforms. Strong communication and collaboration skills with the ability to work effectively across technical and business teams. Enthusiasm for emerging AI technologies and continuous learning. Preferred Qualifications Experience designing and implementing AI agents, autonomous workflows, or enterprise Agentic AI platforms. Hands-on knowledge of agent orchestration frameworks such as LangGraph, AutoGen, CrewAI, Semantic Kernel, Frontier, or similar technologies. Experience with LLM fine-tuning methodologies, including LoRA, PEFT, and other parameter-efficient training approaches. Familiarity with cloud-native AI services and infrastructure on AWS, Azure, or Google Cloud Platform. Exposure to Java, JavaScript, and enterprise application development environments. Knowledge of MLOps, LLMOps, model observability, evaluation frameworks, and production monitoring. Experience implementing AI governance standards, security controls, compliance frameworks, and responsible AI practices. Must-Have Skills Generative AI Large Language Models (LLMs) LangChain LangGraph RAG (Retrieval-Augmented Generation) MCP (Model Context Protocol) Agentic AI / AI Agents Python Vector Databases Prompt Engineering OpenAI / Anthropic / Hugging Face Production AI Application Development This version reads more like a modern enterprise AI engineering job description while maintaining all original requirements and expectations.