SUMMARY:
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POSITION INFO:
ENVIRONMENT:
DESIGN, develop, and deploy Agentic AI systems and LLM-powered applications in production environments as the next Junior-Mid Agentic AI Engineer wanted by a provider of cutting-edge Tech Applications. You will build and optimize RAG (Retrieval-Augmented Generation) pipelines while working with the ML Engineering team on model evaluation, testing, and continuous improvement. Applicants will need 2-3 years of professional experience in AI/ML Engineering or a closely related role. At least one production-level Agentic project — you've built, deployed, and maintained an agent-based system that serves real users or real workloads. You will also require practical experience with RAG architecture & LLM application development.
DUTIES:
- Design, develop, and deploy agentic AI systems and LLM-powered applications in production environments.
- Build and optimize RAG (Retrieval-Augmented Generation) pipelines, including document ingestion, chunking strategies, embedding models, and retrieval mechanisms.
- Integrate and manage vector databases (e.g., Pinecone, Weaviate, Qdrant, Milvus, ChromaDB) for efficient similarity search and knowledge retrieval.
- Develop and maintain Backend services and APIs (primarily in Python) to serve AI models and agent workflows.
- Work with the ML Engineering team on model evaluation, testing, and continuous improvement.
- Contribute to the design of agentic architectures, tool-use patterns, and orchestration frameworks.
- Implement guardrails, monitoring, and observability for LLM-based systems in production.
- Collaborate on MLOps practices including model registry, experiment tracking, and CI/CD for ML pipelines.
- Stay current with the rapidly evolving LLM and agentic AI landscape, evaluating new tools, models, and techniques for adoption.
REQUIREMENTS:
- 2–3 Years of professional experience in AI/ML Engineering or a closely related role. At least one production-level Agentic project — you've built, deployed, and maintained an agent-based system that serves real users or real workloads.
- Solid foundation in general Machine Learning — supervised/unsupervised learning, model training, evaluation metrics, and data preprocessing.
- Hands-on experience with LLM application development — prompt engineering, fine-tuning, function/tool calling, and structured output generation.
- Working knowledge of the agentic stack — agent frameworks, tool integration, memory management, planning and reasoning patterns, and multi-step orchestration.
- Practical experience with RAG architecture — end-to-end pipeline design, embedding models, retrieval strategies, and re-ranking.
- Exposure to vector databases — setup, indexing, querying, and performance tuning.
- Strong Python skills — clean, well-structured, production-quality code. Comfortable with async programming, REST APIs, and standard data/ML libraries.
