AI & Agentic AI Developer
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Role details
Tech stack
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Job description
Agentic AI Development: Design, build, and deploy autonomous AI agents and multi-agent orchestration pipelines using modern cognitive intelligence frameworks.
Workflow Automation: Develop and integrate automated enterprise workflows leveraging tools like MS Power Automate Flow to streamline complex business logic.
API & System Integration: Connect AI workflows seamlessly to external enterprise data estates, modern data warehouses, and legacy platforms using REST APIs with secure authentication management (OAuth 2.0).
Data & NLP Processing: Utilize the LangChain library to build reliable Retrieval-Augmented Generation (RAG) pipelines, semantic memory architectures, and advanced natural language processing tools.
Optimization & Monitoring: Conduct iterative performance testing, refine prompt strategies, build monitoring dashboards, and guardrail agents against security vulnerabilities like prompt injection.
Cross-Functional Collaboration: Partner with solution architects, pre-sales engineers, and client delivery teams to translate client business needs into solid technical blueprints.
Requirements
We are seeking a passionate and driven AI & Agentic AI Developer with 1 to 2 years of hands-on experience to join our rapidly growing team. In this role, you will bridge the gap between AI theory and enterprise execution. You will be responsible for designing, building, and deploying autonomous multi-agent systems, integrating complex enterprise data platforms, and developing automated workflows., Programming: High proficiency in Python for core scripting, application development, and workflow automation.
AI Frameworks: Practical experience with LangChain, LlamaIndex, or similar multi-agent orchestration tools and tool-calling loops.
AI Low-Code & Cloud Studios: Hands-on familiarity with Microsoft Copilot Studio, Azure AI Studio, OpenAI API, or Anthropic.
Data & Infrastructure (Required Exposure):
Familiarity with data manipulation formats (JSON parsing) and vector databases.
Hands-on exposure to one or more major cloud platforms: Azure, AWS, or Google Cloud Platform (Google Cloud Platform).
Practical exposure to cloud data platforms like Databricks and Snowflake.
Integration: Understanding of RESTful API development, data schemas, and secure authentication protocols.
Qualifications & Professional Attributes
Education: Bachelor’s degree in Computer Science, Software Engineering, Data Science, or a related technical field.
Experience: 1-2 years of professional development experience, focusing on AI, automation, cloud infrastructure, or data analytics platforms.
Problem-Solving: Strong critical-thinking abilities with the capacity to turn vague business logic into deterministic technical steps.
AI Security Awareness: Basic understanding of AI safety principles, data privacy guardrails, and secure coding practices.
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