Artificial Intelligence Engineer

AI Engineer

As a AI Engineer specializing in Generative AI, LLMs, and MLOps, you will design and deploy scalable RAG 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.

Roles & Responsibilities

• 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.