Highlights
Join our innovative team and work on cutting-edge projects using the latest technologies in AI and automation.
Description
Job Summary
pWe are seeking an Agentic AI Engineer to join our team and contribute to the development of advanced autonomous AI agents. The ideal candidate will have a passion for AI and experience in integrating large language models (LLMs) into backend systems, implementing RAG pipelines, and automating workflows using n8n.
Responsibilities
- Build and deploy AI agents and multi-agent pipelines
- Integrate LLMs into backend APIs using Python
- Implement RAG pipelines with vector databases
- Create AI automation workflows using n8n
- Deploy models on AWS and maintain CI/CD pipelines
- Fine-tune open-source LLMs using LoRA / QLoRA
- Support frontend (React.js) integration with AI APIs
- Analyse data and monitor model performance
Required Skills
- Python programming
- Experience in AWS services
- Familiarity with LLM APIs
- Knowledge of vector databases
- CI/CD pipeline management
Required Skills Explained
- Python: Essential for developing AI agents and integrating LLMs.
- FastAPI / Flask: Required for building APIs that communicate with AI systems.
- React.js basics: Necessary for frontend integration of AI functionalities.
- LLM APIs - OpenAI, Claude, Hugging Face: Knowledge of these platforms is crucial for developing and integrating language models.
- Agentic AI - LangChain, LangGraph, AutoGen: Experience with these tools will help in building autonomous AI agents.
- AI Automation - n8n: Familiarity with this tool can automate complex workflows involving AI.
- AWS (EC2, S3, Lambda, SageMaker): Essential for deploying and managing AI models on cloud infrastructure.
- CI/CD - GitHub Actions or Jenkins: Understanding of continuous integration and deployment practices is necessary for maintaining workflow efficiency.
- Vector Databases - Pinecone, FAISS, ChromaDB: Knowledge in these databases aids in storing and querying data efficiently.
- AI/ML model deployment - Docker: This skill helps in containerizing AI models for scalable and consistent deployments.
- LLM Fine-Tuning - LoRA, QLoRA: Experience with these techniques is important for enhancing the performance of pre-trained language models.
Who is this for
pThis role is perfect for a fresh graduate or early-career engineer who is passionate about AI and has a strong technical background in backend development. Ideal candidates should have experience with Python, AWS services, and LLM APIs.
Why This Job is a Good Opportunity
ulliGrowth Potential in a Cutting-Edge Field: Work on state-of-the-art AI technologies that are shaping the future of automation and machine learning.liOpportunity to Innovate: Be part of a team developing autonomous AI agents and integrating LLMs into backend systems, driving innovation in your field.liCareer Development: Join an early-career engineer-friendly environment where you can learn from experienced professionals and grow rapidly.liCompetitive Compensation: Enjoy a competitive salary package that reflects the value of your skills in AI engineering.liFlexible Work Environment: Experience a work culture that values creativity, collaboration, and personal growth.
Interview Preparation Tips
- Understand the Role & Responsibilities: Make sure you thoroughly read and understand the job description to highlight relevant experiences during your interview.
- Practice Coding Challenges: Since Python and API development are key skills, practice coding challenges related to these areas.
- Prepare Examples of Your Work: Have examples ready to discuss how you have applied your skills in previous projects or internships.
- Research the Company: Understand the company's mission, recent projects, and technological focus to showcase your alignment with their goals during the interview.
- Ask Questions: Prepare thoughtful questions about the team, project scope, and career growth opportunities at the company to demonstrate your interest.
Career Growth in This Role
pThe role of an Agentic AI Engineer offers significant growth potential. As you develop expertise in building autonomous agents and integrating LLMs, you can progress into more specialized roles such as lead engineer or technical architect. Continuous learning in emerging technologies like RAG pipelines and AI automation workflows will keep your skills relevant and valuable.pNetworking within the industry is also crucial for career advancement. Attend AI conferences, participate in relevant forums, and engage with other professionals to stay updated on the latest trends and opportunities. Building a strong professional network can open doors to mentorship and collaboration, accelerating your career growth.
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Skills
Frequently Asked Questions
What programming languages are required?Python is essential for this role. Familiarity with other languages like FastAPI / Flask is beneficial.
Do I need experience with React.js?While not a must-have, basic knowledge of React.js can be helpful for frontend integration.
What kind of projects will I work on?You'll work on developing AI agents, integrating LLMs into backend systems, and creating automation workflows.
