Senior Machine Learning Engineer

We are looking for a Senior Java Developer with deep expertise in Google Cloud Platform (GCP) and a strong focus on API development, data scripting and analysis, automation testing, and performance optimization. The ideal candidate will have extensive experience designing, deploying, and managing cloud-based applications using GCP services, driving automation, and ensuring system reliability through robust CI/CD pipelines, monitoring, and alerting. You will collaborate with cross-functional teams to deliver high-quality software solutions, provide production support, and contribute to continuously improving our systems and processes.

Role Overview

We are looking for a Senior AI Engineer who can design, build, and deploy production-ready AI solutions using modern Large Language Models (LLMs), AI agents, and cloud-native architectures.

The ideal candidate combines strong software engineering fundamentals with hands-on experience building scalable AI applications, integrating foundation models, and delivering business value through Generative AI.

Key Responsibilities

• Design, develop, and maintain AI-powered applications using Large Language Models (LLMs) and Generative AI technologies.
• Build AI agents and Retrieval-Augmented Generation (RAG) solutions to enable intelligent workflows and knowledge-based applications.
• Integrate leading AI platforms such as Azure OpenAI, Amazon Bedrock, Google Vertex AI, or similar services.
• Develop scalable backend services and APIs using Python and modern frameworks such as FastAPI.
• Collaborate with frontend engineers to deliver end-to-end AI applications using technologies such as React.
• Design prompt engineering strategies to improve model accuracy, reliability, and user experience.
• Implement intelligent routing, semantic search, vector databases, and knowledge retrieval solutions.
• Deploy and manage cloud-native AI applications using AWS and Infrastructure as Code tools such as Terraform.
• Build and maintain CI/CD pipelines, containerized applications, and cloud infrastructure using Docker and DevOps best practices.
• Evaluate emerging AI frameworks, tools, and models to continuously improve platform capabilities.
• Collaborate with Product Managers, Architects, and Engineering teams to translate business requirements into scalable AI solutions.
• Mentor engineers and contribute to technical leadership, architecture discussions, and engineering best practices.

• 10+ years of experience in Software Engineering with recent hands-on experience building Generative AI solutions.
• Strong experience with Python and REST API development.
• Experience developing production AI applications using Large Language Models (LLMs).
• Hands-on experience with AI agent frameworks such as LangChain, CrewAI, or similar technologies.
• Experience implementing Retrieval-Augmented Generation (RAG) architectures.
• Experience integrating AI platforms such as Azure OpenAI, Amazon Bedrock, Google Vertex AI, or equivalent services.
• Strong understanding of prompt engineering techniques and AI application design patterns.
• Experience developing scalable cloud applications on AWS.
• Experience with Docker, Terraform, CI/CD pipelines, and Infrastructure as Code.
• Experience with SQL and NoSQL databases.
• Familiarity with React or modern frontend technologies.
• Experience working within Agile software development environments.
• Strong understanding of software architecture, API design, and distributed systems.
• Experience working in cross-functional and multicultural teams.

• Strong communication skills: able to clearly explain complex AI concepts to both technical and non-technical audiences.
• Proactive mindset: identifies opportunities for innovation and continuously explores new AI technologies.
• Ownership and accountability: takes responsibility for delivering reliable, scalable, and maintainable AI solutions.
• Collaborative attitude: works effectively across product, engineering, architecture, and business teams.
• Adaptability: thrives in a rapidly evolving AI landscape and embraces continuous learning.
• Attention to detail: prioritizes quality, security, observability, and responsible AI practices.
• Customer-oriented thinking: focuses on solving real business problems through practical AI solutions.
• Continuous learner: stays current with advancements in LLMs, AI frameworks, cloud services, and software engineering best practices.