Enterprise AI Solutions Developer || 100% Interview || Onsite interview required

Note: Final interview MUST be completed in person. About the Role We are seeking an innovative Enterprise AI Solutions Developer to design, develop, and implement AI-driven solutions that solve complex business challenges. In this role, you will take AI initiatives from problem definition through deployment, leveraging large language models (LLMs), retrieval-augmented generation (RAG), and agent-based frameworks. You will partner with business and technology teams to integrate intelligent automation into enterprise systems, specifically focusing on cloud-based AI platforms like Azure AI.

Primary Responsibilities

• End-to-End AI Development: Design, prototype, test, deploy, and monitor AI applications and intelligent automation solutions.
• Strategic Integration: Integrate AI capabilities with enterprise systems, databases, APIs, and existing business processes.
• Prompt & Workflow Engineering: Develop and refine prompts, workflows, agents, and retrieval strategies to maximize AI effectiveness.
• AIOps Pipeline Creation: Design AI-driven monitoring pipelines that ingest server, database, and application logs to predict anomalies, suggest real-time resolutions, and automate root-cause analysis (RCA).
• Model Evaluation: Evaluate and select appropriate AI models, frameworks, and architectures to meet defined business objectives.
• Governance & Compliance: Ensure all solutions comply with enterprise security, privacy, and Responsible AI standards.
• Cross-Functional Collaboration: Partner with technology managers and business users to identify opportunities for AI-driven improvements, documenting designs and key decisions along the way.

Required Skills & Experience (2 - 10 Years)

• Core AI & GenAI: Deep hands-on experience with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, prompt engineering, and model orchestration frameworks.
• Platforms & Tools: Proven experience with Azure AI, Microsoft Copilot Studio, Gemini Enterprise, and Spark AI.
• Programming & Data: Strong proficiency in Python (including AI-related libraries) and SQL, with robust data analysis skills.
• Systems Integration: Experience integrating AI solutions with enterprise systems, APIs, databases, and business applications.
• Engineering Best Practices: Familiarity with Git, DevOps, CI/CD pipelines, and version control.
• AI Fundamentals: A solid understanding of AI/ML concepts, model evaluation techniques, limitations, and responsible AI practices.

Nice to Have

• Experience applying AI/ML specifically to IT operations (AIOps), including predictive log analysis and automated incident remediation.