Senior MLOps & AI Developer

Job Title: Senior MLOps & AI Developer Job Description We are seeking a highly accomplished Senior MLOps & AI Developer who combines a strong development background with deep experience in designing and managing AI/ML infrastructure. In this role, you will develop, deploy, and maintain scalable AI solutions focused on high accuracy. Your expertise will not only drive robust MLOps pipelines and hybrid cloud solutions but also extend to full-stack development, ensuring an end-to-end AI application experience. This includes handling database solutions, optimizing data workflows, and developing intuitive front-end applications using Angular and related technologies. Key Responsibilities

• AI/ML Infrastructure Development: Plan, build, and continuously enhance infrastructure for model training, deployment, and live monitoring, ensuring scalability, high availability, and robust security.
• Design End-to-End MLOps Pipelines: Develop and optimize CI/CD pipelines that automate the process of building, testing, and deploying AI models, emphasizing cost-efficiency, performance, and seamless integration.
• Scalable AI Solutions with High Accuracy: Engineer and implement AI solutions that focus on driving higher model accuracy through advanced techniques such as chunking and embedding. Work with multiple large language models (LLMs) to create optimal solutions.
• Database Management & Indexing: Set up and manage various data storage systems including indexers, PostgreSQL, MySQL, and other non-SQL databases to support dynamic and scalable AI workflows.
• Hybrid Cloud & On-Premise Integration: Manage and optimize resources across major cloud platforms (with Azure as the primary focus) along with on-premise systems, addressing the unique challenges of hybrid environments.
• Full-Stack Development for AI Applications: Develop responsive, user-friendly front-end applications using Angular and related technologies, ensuring a seamless end-to-end AI application experience that complements the back-end infrastructure.
• Collaboration Across Teams: Work closely with AI/ML developers, data scientists, and DevOps teams, driving innovation and troubleshooting challenges to streamline development, deployment, and performance optimization.
• Security & Performance Optimization: Implement best practices in networking, security protocols, and performance tuning to maintain a secure and efficient operational environment.

Required Skills & Qualifications

• Proven Development Expertise: 8-10 years of hands-on experience in software development, with strong proficiency in programming (e.g., Python) and experience in agile software development practices such as version control, code reviews, and testing.
• MLOps & Infrastructure Proficiency: Demonstrable expertise using tools like Kubernetes, Docker, Terraform, and managing cloud resources (with a strong command of Azure). Experience with hybrid cloud architectures and effectively handling on-premise environments is essential.
• AI & ML Integration: Deep understanding of AI/ML workflows including model training, deployment, and performance tuning. Familiarity with advanced techniques like chunking and embedding, and proven capability in integrating multiple LLM models to drive higher accuracy.
• Database & Indexer Expertise: Experience in setting up and managing relational databases such as PostgreSQL and MySQL, as well as non-SQL databases. Ability to design and implement efficient indexing solutions conducive to AI data workloads.
• Front-End Development Skills: Proficiency in Angular and related web development technologies (e.g., TypeScript, HTML, CSS) to create integrated, end-to-end AI application interfaces.
• Technical Acumen: Strong background in networking, security protocols, and system performance optimization ensuring robust, secure, and high-performing AI/ML systems.

Preferred Qualifications

• Hybrid Systems Management: Experience managing complex infrastructures that span on-premise and cloud-based resources, demonstrating a clear grasp of the nuances underlying hybrid environments.
• GPU-Based and Advanced AI Deployment: Prior exposure to GPU-based processing and AI model deployment strategies, leveraging advanced hardware capabilities for optimized training and inference.
• Machine Learning Frameworks: Familiarity with leading machine learning frameworks (e.g., TensorFlow, PyTorch, MLflow) to better collaborate with data science teams and enhance AI solution development.