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/Google Cloud Platform).
• 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/Node.js).
• 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.