AI Developer

Inteldot is seeking an experienced AI Developer to support the design, development, integration, and implementation of Artificial Intelligence (AI) and intelligent automation solutions for a leading LifeSciences company. The role will focus on leveraging Generative AI, Large Language Models (LLMs), AI agents, workflow automation, and enterprise integration technologies to improve operational efficiency, streamline business processes, and support digital transformation initiatives.

• →Design, develop, test, and deploy AI-powered applications and intelligent automation solutions aligned with business requirements.
• →Develop solutions leveraging Generative AI, Large Language Models (LLMs), and AI agents to support business processes and operational workflows.
• →Build and integrate AI capabilities with existing enterprise applications, databases, APIs, and automation platforms.
• →Develop automated workflows to reduce manual activities, improve process efficiency, and enhance data accessibility.
• →Evaluate and implement AI technologies using enterprise-approved platforms and tools.
• →Collaborate with business stakeholders to translate operational challenges into technical requirements and practical AI solutions.
• →Develop proof-of-concept (POC) solutions, prototypes, and scalable applications.
• →Implement AI-assisted document processing, information retrieval, data analysis, and workflow orchestration capabilities.
• →Establish testing, performance evaluation, monitoring, and documentation practices for AI solutions.
• →Ensure AI applications comply with applicable enterprise cybersecurity, data privacy, information governance, and responsible AI requirements.
• →Support deployment, troubleshooting, maintenance, and continuous improvement of implemented solutions.
• →Prepare technical documentation and provide knowledge transfer to business and technical teams.

• Bachelor's degree in Computer Science, Software Engineering, Information Technology, Artificial Intelligence, or a related technical discipline.
• Professional experience in software development, AI application development, or intelligent process automation.
• Strong programming skills in Python and experience developing applications using APIs.
• Hands-on experience integrating Large Language Models (LLMs) or Generative AI services into business applications.
• Experience with REST APIs, JSON, databases, and enterprise system integrations.
• Understanding of AI agents, prompt engineering, Retrieval-Augmented Generation (RAG), and workflow orchestration concepts.
• Experience developing, testing, debugging, and deploying software applications.
• Knowledge of secure software development practices, authentication, data protection, and access controls.
• Strong analytical, problem-solving, and technical documentation skills.
• Ability to work collaboratively with cross-functional business and IT teams.
• Strong communication skills in English.

• Experience with enterprise AI platforms such as Azure OpenAI, OpenAI API, Amazon Bedrock, or similar technologies.
• Experience with AI development frameworks such as LangChain, LangGraph, Semantic Kernel, or equivalent tools.
• Familiarity with Microsoft Copilot, GitHub Copilot, or enterprise AI development environments.
• Experience with workflow automation and orchestration tools such as Power Automate, UiPath, or n8n.
• Experience with cloud environments, including Microsoft Azure or AWS.
• Familiarity with Databricks, SQL, or enterprise data platforms.
• Experience implementing AI agents, RAG applications, document intelligence, or knowledge management solutions.
• Experience integrating AI solutions with enterprise applications such as SharePoint, ServiceNow, SAP, or similar systems.
• Knowledge of CI/CD pipelines, Git, application monitoring, and software deployment practices.
• Previous experience in pharmaceutical, biotechnology, life sciences, or other regulated industries.
• Familiarity with GxP, Computer System Validation (CSV), data integrity, and AI governance requirements.

• Generative AI and Large Language Models (LLMs)
• AI Agents and Agentic Workflows
• Python and API Development
• Retrieval-Augmented Generation (RAG)
• Business Process Automation
• Enterprise Application Integration
• Cloud-Based AI Services
• Data Integration and Processing
• AI Testing and Performance Evaluation
• Responsible AI, Security, and Governance