Full Stack AI/ML Developer

Company Overview

AI/ML Development

• Research, design, and implement advanced AI solutions, including LLMs, retrieval-augmented generation (RAG), and agent-based systems.
• Build scalable training, fine-tuning, and inference pipelines for machine learning and generative AI models.
• Conduct feasibility studies and align AI/ML architectures with business objectives.
• Develop computer vision, NLP, and multi-modal AI pipelines as needed.
• Monitor and optimize deployed models for performance, reliability, and cost efficiency.

Data Engineering & Processing

• Design and maintain robust data pipelines for structured and unstructured data ingestion at scale.
• Transform, clean, and normalize raw data into high-quality analytical formats for downstream AI use.

Full Stack & Visualization

• Build user-facing dashboards, tools, and interfaces for AI-driven insights and workflows.
• Design intuitive and interactive visualizations for exploring data, relationships, and model outputs.
• Collaborate with stakeholders to translate requirements into functional and accessible solutions.

DevOps & Cloud Infrastructure

• Architect and manage cloud-native environments to support AI/ML and full-stack applications.
• Ensure scalability, monitoring, and high availability of production systems.
• Set up CI/CD pipelines for continuous integration and deployment of both code and AI models.

• 3–5 years of professional experience in software engineering and preferrable experience in data engineering and applied AI/ML.
• Experience in end-to-end AI solution development—from data pipelines and modeling to deployment and integration.
• Experience building and deploying systems involving LLMs, RAG, NLP, or multi-modal AI.

• Proficiency in Python for backend, data, and analytical workflows. 

• Background in or extensive exposure to full-stack web development, including building APIs, services, and interactive UIs.
• Hands-on experience with cloud platforms (AWS, Azure, or GCP) and containerized environments.
• Understanding of machine learning concepts (neural networks, transformer architectures, reinforcement learning, etc.).
• Grasp of distributed systems, event-driven workflows, and real-time data processing.

• Developing investigative, analytical, or visualization tools for unstructured data.
• Working with knowledge graphs, large-scale search, or social intelligence platforms.
• Familiarity with reinforcement learning or agentic system design.
• Experience with JavaScript/TypeScript, Java, etc.