GenAI engineer Developer

Job Summary:

We are looking for motivated and enthusiastic trainees with a strong foundation in programming and emerging technologies. The ideal candidates should have hands-on knowledge of modern programming languages familiarity with AI-enabled tools and strong communication skills to effectively collaborate with teams and stakeholders

Key Responsibilities:

• Develop and Enhance GenAI Solutions : Build fine-tune and optimize LLM-based applications ensuring high accuracy reliability and performance.
• Implement RAG and AI Pipelines : Create end-to-end GenAI workflows using vector databases embeddings and cloud-based model orchestration.
• Integrate AI into Products : Work with APIs microservices and backend systems to embed GenAI features into applications at scale.
• Monitor and Improve Model Performance : Conduct evaluations A/B tests and model drift checks while applying prompt engineering and optimization techniques.
• Collaborate and Ensure Responsible AI : Partner with cross-functional teams to implement secure compliant and ethically aligned AI systems

Qualification and Specialization:

Required Skills & Qualifications:

• Design build and optimize GenAI solutions using LLMs transformer-based models and multimodal architectures for real-world business applications.
• Fine-tune and evaluate pre-trained models (e.g. GPT Llama Claude Gemini) using domain-specific datasets to improve accuracy and performance.
• Develop scalable AI pipelines including data ingestion model training inference services and deployment on cloud platforms (Azure/AWS/GCP).
• Implement Retrieval-Augmented Generation (RAG) using vector databases (Pinecone FAISS Chroma DB Azure AI Search) to enable enterprise-grade QA and summarization systems.
• Integrate GenAI models into applications via APIs SDKs microservices or custom backend frameworks (Python/).
• Optimize model performance through prompt engineering model compression quantization and latency reduction techniques.
• Collaborate with cross-functional teams (data engineers product owners designers) to translate business needs into AI capabilities.
• Ensure security compliance and responsible AI practices including data privacy model monitoring bias mitigation and ethical guidelines.
• Conduct experimentation and benchmarking to evaluate model performance run A/B tests and document results for continuous improvement.
• Stay up to date with the latest advancements in generative AI LLM frameworks and open-source tools while contributing to internal innovation initiatives.