Senior Generative AI (Gen AI) Engineer

Role overview

Responsibilities

• check_circle Design, develop, and optimize Generative AI solutions (LLMs, RAG systems, AI agents).
• check_circle Fine-tune, evaluate, and deploy large language models for enterprise use cases.
• check_circle Build and maintain end-to-end AI pipelines, from data ingestion to inference.
• check_circle Implement Retrieval-Augmented Generation (RAG) using vector databases and knowledge stores.
• check_circle Integrate GenAI solutions with existing systems via APIs and microservices.
• check_circle Optimize model performance, latency, and cost on cloud platforms (OCI, AWS, Azure, or GCP).
• check_circle Collaborate with product, data, and engineering teams to translate business needs into AI solutions.
• check_circle Ensure AI solutions follow best practices in security, governance, and responsible AI.
• check_circle Stay up to date with the latest developments in GenAI, LLMs, and AI tooling.
• check_circle Minimum 5 years of professional experience in AI / Machine Learning.
• check_circle Master’s degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, or a related field.
• check_circle Proficiency in programming languages such as python , Java , and Ruby.
• check_circle Strong experience with Large Language Models (LLMs) (e.g., GPT, LLaMA, Mistral, etc.).
• check_circle Hands-on experience with Python and AI/ML frameworks (PyTorch, TensorFlow).
• check_circle Solid understanding of NLP, deep learning, and transformer architectures.
• check_circle Experience with RAG architectures, vector databases (FAISS, OpenSearch, Pinecone, etc.).
• check_circle Strong software engineering skills (APIs, REST, microservices, Docker, Kubernetes).
• check_circle Experience deploying AI workloads on cloud platforms (OCI preferred).
• check_circle Excellent problem-solving and analytical skills.
• check_circle Excellent command of English (written and spoken).

Preferred qualifications

• Experience with GPU-based training and inference.
• Familiarity with MLOps / LLMOps practices.
• Experience in regulated or enterprise environments.
• Knowledge of AI governance, ethics, and compliance frameworks.

Benefits

• check_circle Opportunity to work on cutting-edge Generative AI projects.
• check_circle Collaborative, innovation-driven environment.
• check_circle Competitive compensation package.
• check_circle Access to modern AI infrastructure and cloud resources.
• check_circle Professional growth and learning opportunities.