Artificial Intelligence Engineer

As a AI Engineer specializing inGenerative AI, LLMs, and MLOps, you will design and deploy scalableRAG systems, Agentic Workflows, and Deep Learning models within cloud-native FinTech environments. This role bridges the gap between research and production, requiring hands-on expertise in LangChain, Vector Databases, and Vertex AI/SageMaker to automate complex decision processes. You will be responsible for the full lifecycle of AI solutions—from prompt engineering and Fine-tuning to building robust CI/CD pipelines and ensuring Responsible AI governance.

Must Have Technical/Functional Skills Design, develop, train, and deploy machine learning, deep learning, and Generative AI models to solve complex business problems across payments and FinTech platforms. Build and maintain scalable ML and GenAI pipelines using cloud‑native tools, ensuring reliability, performance, and production readiness. Implement GenAI solutions using platforms such as Google Cloud Vertex AI, AWS SageMaker/Bedrock, and Snowflake Cortex, aligned with enterprise standards.

Apply advanced techniques including prompt engineering, Retrieval‑Augmented Generation (RAG), model fine‑tuning, and RLHF to improve model accuracy, robustness, and business relevance. Develop and deploy AI agents and agentic workflows using frameworks such as LangChain, LangGraph, AgentSpace to automate multi‑step decision and reasoning processes. Integrate vector databases and semantic retrieval systems (e.g., PGVector) to support memory, search, and contextual grounding in GenAI applications.

Collaborate closely with data scientists, product managers, and engineering teams to translate business requirements into end‑to‑end AI‑powered solutions. Implement and enforce MLOps best practices, including CI/CD pipelines, model versioning, monitoring, retraining, and lifecycle management. Optimize models and pipelines for scalability, latency, cost efficiency, and reliability in high‑throughput, production cloud environments.

Ensure AI solutions adhere to security, privacy, and responsible AI principles, including governance, bias awareness, and safe model usage. Monitor model performance and data drift, performing continuous improvements based on feedback, metrics, and real‑world usage patterns. Stay current with AI research, emerging tools, and industry trends, evaluating new technologies for enterprise adoption.

Produce clear technical documentation, reusable components, and reference implementations to support team knowledge sharing and reuse. Contribute as a hands‑on engineer in cross‑functional Agile teams, supporting rapid experimentation, iterative delivery, and operational excellence. #J-18808-Ljbffr