Subcontractor

Gen AI Engineer

Location: Any BSL

Key Responsibilities

• Design, develop, and implement Generative AI solutions to address business and technical challenges.
• Build and integrate AI agents, workflows, and orchestration frameworks for enterprise use cases.
• Develop proof-of-concepts (POCs) and prototypes to validate GenAI capabilities and business value.
• Collaborate with cross-functional teams to gather requirements and translate them into scalable AI solutions.
• Implement, test, and optimize LLM-based applications, including prompt engineering, RAG, and agent-based systems.
• Identify potential risks, edge cases, and performance considerations during solution design and implementation.
• Support deployment, monitoring, and continuous improvement of AI applications in production environments.
• Troubleshoot technical issues, take ownership of problem resolution, and drive solutions to completion.
• Stay current with emerging trends, tools, and best practices in Generative AI and agentic AI systems.

Requirements & Tech Stack

• Bachelor’s or Master’s degree in Computer Science, Information Systems, or equivalent experience.
• Strong programming skills, preferably in Python, with the ability to design and implement efficient solutions.
• Hands-on experience building Generative AI applications and agent-based systems.
• Good understanding of LLMs, prompt engineering, RAG architectures, and AI orchestration frameworks.
• Experience developing and delivering POCs and technical prototypes.
• Familiarity with cloud platforms such as Azure, AWS, or GCP.
• Strong analytical and problem-solving skills.
• Ability to work independently and take ownership of technical challenges.
• Good communication and collaboration skills.
• Demonstrated eagerness to learn new technologies and adapt to evolving AI ecosystems.

Preferred Skills

• Experience with frameworks such as LangChain, LangGraph, CrewAI, AutoGen, or similar agent frameworks.
• Knowledge of vector databases, embeddings, and retrieval systems.
• Understanding of application scalability, monitoring, and production deployment considerations.
• Experience in evaluating AI solutions for reliability, performance, and business impact.
• Exposure to handling edge cases, failure scenarios, and responsible AI practices.