GenAI Engineer | LLMs, NLP & Cloud (MLOps)

Job Summary

Required Experience and Qualifications

• Bring at least 5 years of AI, machine learning, or data science experience, including at least 3 years in GenAI or NLP.
• Deliver AI or machine learning solutions in production environments.
• Hold a bachelor’s or master’s degree in Computer Science, Data Science, AI, or a related field, or qualify through an alternative portfolio or certification pathway.
• Collaborate effectively with product, data science, engineering, and security teams.

Required Technical Skills

• Use generative AI, large language models, Python, RAG pipelines, serverless or event-driven architectures, vector databases, and embeddings.
• Deploy AI models on AWS, Azure, or GCP and apply basic MLOps concepts.
• Use Git, CI/CD concepts, PyTorch, TensorFlow, and Hugging Face Transformers.
• Apply model training, fine-tuning, evaluation, data preprocessing, data pipelines, model security, privacy, and governance.

Preferred Technical Skills

• Experience with Docker, Kubernetes, AI CI/CD tooling, model monitoring, bias mitigation, AI safety, and auditable governance.
• Familiarity with R, Java, C++, LangChain, SpaCy, OpenAI API patterns, managed AI services, NoSQL, data warehousing, and multi-cloud deployments.

Overall Responsibilities

• Design, develop, and optimize GenAI solutions and autonomous AI workflows.
• Lead AI model implementation, deployment, governance, and CI/CD integration on cloud platforms.
• Mentor junior AI engineers and promote AI development, MLOps, and responsible AI best practices.
• Evaluate new AI technologies and architectures and translate findings into business improvements.
• Collaborate with product, data, and platform teams to build scalable AI solutions.
• Maintain AI architecture, deployment guidelines, model governance documentation, and delivery automation.

Day-to-Day Activities

• Design, train, fine-tune, and deploy GenAI and NLP models and autonomous AI components.
• Build and maintain training, inference, and monitoring pipelines in cloud environments.
• Evaluate AI techniques, tools, and platforms and lead proofs of concept.
• Monitor model performance, detect drift, and implement retraining or adjustments.
• Document model architecture, data pipelines, deployment steps, governance, risk, and compliance activities.

Professional Competencies