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Tier-1 employer and mid-level AI role increases applicants, but niche LLM/MLOps skills limit competition.
Skills are transferable across industries but require enterprise integration and compliance experience, so sensitivity is medium.
Explicit 4–6 years plus mandatory LLM, MLOps, Python, cloud, and integration skills create strict filters.
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Develop, fine-tune, and deploy AI models including large language models (LLMs) and implement prompt engineering to improve AI accuracy and reliability.
Design, prototype, and integrate AI solutions securely with enterprise systems via APIs and manage end-to-end data pipelines for AI model operations.
Deploy and scale AI solutions on cloud platforms (Azure, AWS, GCP) using DevOps/MLOps practices ensuring compliance, security, and measurable business impact.
4-6 years of advanced experience in developing, deploying, and maintaining AI and machine learning solutions in enterprise environments.
Bachelor’s degree in Computer Science, Engineering, Data Science, or related field.
Advanced proficiency in Python programming, AI model deployment, prompt engineering, API integration, cloud platforms (Azure/AWS/GCP), and data engineering.
Workplace: Hybrid working; Certifications such as Microsoft Certified: Azure AI Engineer Associate preferred but not mandatory.
Experienced in enterprise AI solution design with strong capability in aligning technical AI implementations to business strategies and measurable outcomes.
Operates with advanced knowledge of deploying scalable AI solutions using cloud infrastructure and managing ML lifecycle with DevOps/MLOps best practices.
Skilled at engaging clients and stakeholders to translate complex requirements into robust, secure AI integrations within regulated environments.