AI Engineer

About the role

We are seeking a highly skilled and forward-thinking AI Engineer to join our AI Engineering team. This role is ideal for a hands-on technologist with deep expertise in machine learning (ML) and deep learning (DL), and a strong working knowledge of Generative AI technologies.

Responsibilities

• Design, develop, and deploy ML, DL, and Generative AI models that solve complex business problems and deliver measurable value.
• Build and maintain scalable ML pipelines and agentic workflows using modern frameworks and cloud-native tools.
• Collaborate with data scientists, product managers, and engineers to translate business requirements into AI-powered solutions.
• Implement and optimize AI solutions using platforms such as GCP Vertex AI, AWS Bedrock/SageMaker, and Snowflake Cortex.
• Apply techniques such as prompt engineering, RAG (Retrieval-Augmented Generation), fine-tuning, and RLHF to enhance model performance.
• Develop and deploy autonomous AI agents using frameworks like LangChain, LangGraph, and AgentSpace.
• Integrate vector databases (e.g., PGVector) and LLM orchestration tools to support retrieval and memory in generative systems.
• Ensure robust MLOps practices including CI/CD, monitoring, versioning, and lifecycle management of models.
• Stay current with AI research and industry trends, and evaluate emerging tools and techniques for enterprise adoption.
• Contribute to internal knowledge sharing, documentation, and best practices for responsible and ethical AI development.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• 4+ years of experience in AI/ML engineering, with a strong foundation in ML/DL algorithms and systems.
• Proficiency in Python and ML libraries such as TensorFlow, PyTorch, and Transformers.
• Hands-on experience with Generative AI models (e.g., GPT, Mistral, Claude) and agentic AI systems.
• Familiarity with NLP, conversational AI, and LLM-based applications.
• Experience with vector search and semantic retrieval technologies.
• Strong understanding of MLOps, including model deployment, monitoring, and retraining.
• Experience building production-grade AI systems at scale in cloud environments.
• Hands-on experience with development on Google Cloud Platform.
• Excellent problem-solving, communication, and collaboration skills.

Qualifications

• Experience with prompt engineering, RLHF, and LLM evaluation techniques.
• Understanding of AI governance, safety, and responsible AI principles.
• Experience with reinforcement learning, multi-agent systems, and autonomous workflows.
• Experience with big data technologies (e.g., Apache Spark, Kafka) and real-time data processing.
• Experience with data engineering techniques and data warehousing platforms (e.g. BigQuery, Snowflake).
• Contributions to open-source AI projects or publications in the AI/ML field.
• Familiarity with CI/CD pipelines, infrastructure-as-code, and cloud-native AI tooling.