Enterprise Generative AI Engineer (Lead Mid-Level)

About the job

About The Role We are seeking a small, high-impact team of Enterprise Generative AI Engineers to design, build, and deploy production-grade AI solutions across multiple industry verticals. This role is ideal for professionals experienced in delivering end-to-end AI systems from prototyping to scalable deployment. The Team Will Include • A Senior Lead responsible for architecture, technical direction, and stakeholder engagement • Mid-Level Engineers focused on hands-on development and implementation You will work in a fast-paced, cross-functional environment, collaborating with business stakeholders, product teams, and engineering groups to deliver scalable AI solutions. Key Responsibilities AI Solution Development • Design and implement LLM-powered applications such as: • Retrieval-Augmented Generation (RAG) pipelines • AI agents and multi-agent systems • Chatbots and generative AI integrations • Integrate with commercial and open-source LLMs (OpenAI, Anthropic, Llama, Mistral) ML Engineering Pipelines • Build and optimize ML/data pipelines for: • Training, fine-tuning, and evaluation • Embeddings and vector search workflows • Develop scalable and reusable AI/ML components Infrastructure MLOps • Architect and manage production-grade AI infrastructure • Implement: • Containerization (Docker, Kubernetes) • CI/CD pipelines for ML workflows • Model monitoring, logging, and scaling strategies Stakeholder Collaboration • Engage with business stakeholders to: • Understand requirements • Identify AI use cases • Translate ambiguous needs into technical solutions • Collaborate with cross-functional teams to align AI solutions with business goals Best Practices Documentation • Establish standards for: • Prompt engineering • Model evaluation • Responsible AI practices • Document system architecture, workflows, and operational runbooks Required Qualifications Senior Lead (7+ Years Experience) • Proven experience delivering enterprise-scale AI/ML systems in production • Strong expertise in: • Python, PyTorch/TensorFlow • Modern ML/AI frameworks and ecosystems • Hands-on experience with cloud platforms (AWS, GCP, or Azure) • Deep knowledge of: • Kubernetes, Docker, and container orchestration • MLOps and scalable ML infrastructure • Expertise in LLM application patterns: • RAG, embeddings, vector databases, function calling • Strong leadership and communication skills: • Ability to lead architecture decisions • Mentor engineers • Drive stakeholder discussions and discovery sessions Mid-Level Engineers (3-7 Years Experience) • Hands-on experience building ML models and data pipelines • Proficiency in: • Python • At least one deep learning framework (PyTorch preferred) • Working knowledge of: • Cloud platforms (AWS/GCP/Azure) • Docker and Kubernetes • Familiarity with: • LLM APIs and prompt engineering • RAG and vector database concepts • Ability to: • Work independently • Translate requirements into high-quality implementations