Job details:

• Role: Senior Generative AI Engineer
• Full-time
• Remote
• Start: ASAP
• End: 31/12/2026

Scope:

• Design, develop, and deploy cutting-edge Generative AI solutions using state-of-the-art LLMs and foundation models.
• Fine-tune, optimize, and evaluate Large Language Models for industry-specific use cases.
• Develop AI-powered applications utilizing NLP and Generative AI technologies.
• Build and implement Agentic AI solutions using modern orchestration frameworks.
• Develop Retrieval-Augmented Generation (RAG) solutions leveraging vector databases and knowledge retrieval systems.
• Work with Google Cloud Platform (GCP) services, including Vertex AI, to build scalable and production-ready AI applications.
• Collaborate with cross-functional teams to understand business challenges and design AI-driven solutions.
• Participate in research, prototyping, and innovation initiatives in the rapidly evolving Generative AI ecosystem.
• Implement best practices related to MLOps, LLMOps, model evaluation, monitoring, and deployment.
• Contribute to architectural decisions and technical direction for AI projects.
• Ensure high-quality code through version control, testing, and CI/CD practices.

Skills:

• 4+ years of professional experience in software development, AI, ML, or Data Science.
• Strong programming skills in Python.
• Hands-on experience with Generative AI and Large Language Models (LLMs).
• Experience building AI applications using:
• LangChain
• LangGraph
• Agentic AI frameworks
• Experience with Google Cloud Platform (GCP).
• Hands-on knowledge of Vertex AI.
• Experience working with:
• RAG (Retrieval-Augmented Generation)
• Vector Databases
• Knowledge Retrieval Systems
• Familiarity with vector search technologies such as:
• FAISS
• Pinecone
• ChromaDB
• Weaviate
• Experience with model fine-tuning and optimization techniques.
• Knowledge of prompt engineering best practices.
• Experience using Git and modern software development workflows.
• Understanding of scalable cloud-native architectures.

Nice to have:

• Experience with CI/CD pipelines.
• Knowledge of LLMOps best practices.
• Experience with AI model evaluation and benchmarking.
• Familiarity with RLHF (Reinforcement Learning from Human Feedback).
• Experience with Hugging Face Transformers.
• Knowledge of distributed computing frameworks.
• Experience deploying AI solutions in production environments.
• Familiarity with Kubernetes and containerization technologies.
• Experience with monitoring and observability tools for AI applications.
• Understanding of Responsible AI and AI Governance principles.

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