Generative AI Engineer

Summary

We are seeking a highly skilled and passionate AI Engineer with expertise in Generative AI (GenAI) to develop and implement cutting-edge AI solutions across cloud-native environments. This role combines hands-on technical engineering with strategic leadership, enabling customers to translate AI innovation into scalable products—from ideation to MVP and beyond.

Responsibilities

• Design, build, and deploy GenAI-driven applications using frameworks such as LangChain, OpenAI API, and Hugging Face Transformers.
• Architect and implement scalable solutions within containerized, microservices-based cloud-native environments (e.g., Kubernetes, OpenShift).
• Integrate GenAI models and services seamlessly into existing platforms and services.
• Collaborate with partner teams (Google, Microsoft, AWS, IBM, etc.) on advanced AI solution development.
• Refactor and modernize legacy systems with 12-factor principles and cloud-native best practices.
• Drive innovation in GenAI adoption through internal R&D, PoCs, and pilot programs.
• Stay ahead of AI trends and apply the latest research into practical applications.
• Provide expert consultation on GenAI architecture, deployment strategies, and business use case alignment.
• Facilitate technical workshops, presentations, and strategic planning sessions with clients.
• Serve as a trusted technical advisor on AI/ML integration, helping clients unlock value from AI technologies.

Requirements

• Bachelor's degree in Computer Science, Information Technology, or a related field
• 5 years of software engineering experience, with strong programming skills in Java and Python
• Deep expertise in Generative AI frameworks, including LangChain, OpenAI, Hugging Face Transformers.
• Strong foundation in Natural Language Processing (NLP): tokenization, embeddings, transformers, LLMs.
• Experience with prompt engineering and optimizing AI model interactions.
• Proven success designing and deploying cloud-native solutions using Kubernetes, Docker, and microservices.
• Strong experience with Agile and DevOps practices, working in Jira-based environments.
• Hands-on experience with MLOps (CI/CD for ML, model versioning, monitoring).
• Proficiency in data engineering for AI: ETL, feature pipelines, data labeling, quality control.
• Knowledge of vector databases (e.g., Pinecone, FAISS, Weaviate) and retrieval-augmented generation (RAG) techniques.
• Cloud certifications from AWS, Azure, or Google Cloud Platform (GCP).