Senior AI Engineer

About Company:

The client founded in 1997 and is a global Agentic AI Services company. The client specialize in Product Development, Design, Quality Engineering, Data & Analytics, Security, and Operations, and help enterprises transform into AI-native organizations through our enterprise-grade Agentic AI platform, AiDE®, and comprehensive Vertical AI solutions. They are present in 29 locations globally with 300 clients and 7000+ associates. Four-time winner of the prestigious Gold Stevie® Award for Employer of the Year (2021, 2022, 2023, and 2024). They focus on employees and has resulted in industry-leading Net Promoter Scores (NPS) of 80 and 90, respectively.

An Ideal Candidate:

1. Strong programming fundamentals (OOP, security, performance).
2. Experience building scalable and modular systems using modern frameworks and tools.
3. Proficiency with LLMs, OpenAI/Anthropic APIs, LangChain, Hugging Face Transformers, or similar.
4. GenAI & Agentic AI
5. 2+ years of experience working with GenAI and LLMs.
6. 2+ years of experience in prompt engineering and AI agent architectures.
7. Familiarity with agent frameworks like CrewAI, Autogen, Cursor, or Windsurf.
8. Strong understanding of the Cursor Rule (intent → context → action), a principle for structuring agent behavior, and real-world experience applying it in workflows.
9. Solid understanding of how to construct and pass dynamic context to LLMs, including techniques like session memory and prompt templating.
10. Experience adapting or customizing LLMs is a plus.
11. Familiarity with front-end frameworks or relational databases is a plus.

Key Competencies:

1. Architect and build AI-powered tools using LLMs (e.g., OpenAI, Claude, Mistral, etc.).
2. Integrate and customize AI agents using frameworks like LangChain, CrewAI, or custom solutions.
3. Collaborate with product and research teams to translate requirements into prompt workflows and agent behaviors.
4. Design and implement scalable systems and infrastructure to support AI agents and workflows, using suitable tools and technologies.
5. Design advanced, modular prompt chains that incorporate memory, dynamic context injection, and multi-step reasoning across agent workflows.
6. Experiment with and optimize agentic architectures for real-world tasks (retrieval, generation, summarization, etc.).
7. Collaborate with UI/UX or front-end teams to integrate AI-powered features into user-facing applications as needed.
8. Willingness and ability to quickly learn and adapt to new technologies, frameworks, and tools as needed. Open to working across the stack to support the development of AI-powered solutions. Comfortable stepping beyond core skill sets to contribute wherever needed in the AI application lifecycle.
9. Write unit and integration tests to ensure reliability.
10. Participate in code reviews, CI/CD pipelines, and deploy models/services using best practices.
11. Upskill peers in GenAI, prompt engineering, and best practices.
12. Contribute to knowledge-sharing and architectural reviews across teams.
13. Stay updated with evolving AI ecosystems, tools, and research.