<p>JOB SUMMARY</p> <p>Are you a machine learning enthusiast who thrives on transforming data into impactful insights? Do you dream in algorithms, data pipelines, and model architectures? We're looking for a Machine Learning Engineer who is a true builder-someone who can roll up their sleeves, design, develop, and deploy scalable ML solutions. This isn't just another job; it's an opportunity to be part of a team that is shaping the next generation of AI-driven products that will directly drive growth.</p> <p>Key Responsibilities:</p> <ul> <li>Build the Future: Design, develop, and deploy end-to-end machine learning systems optimized for performance, scalability, and rapid deployment. </li><li>AI-First Development: Leverage AI tools (GitHub Copilot, Cursor AI, ChatGPT, Otter AI) to enhance code efficiency, refactoring, debugging, and documentation. </li><li>Rapid Prototyping: Rapidly prototype and deploy ML models and full-stack applications using AI-assisted development tools. </li><li>ML & Data Pipelines: Build and optimize data pipelines for efficient ingestion, transformation, and analysis. </li><li>LLM Integration: Implement and fine-tune large language models (LLMs) like GPT, BERT, and integrate them seamlessly into production systems. </li><li>MLOps Excellence: Design, develop, and implement CI/CD pipelines for continuous deployment and monitoring of machine learning models. </li><li>Cloud-Native Development: Build and maintain cloud-native ML applications on platforms like GCP, AWS, or Azure. </li><li>Collaboration: Work closely with product managers, data scientists, and engineers to translate business requirements into scalable ML solutions. </li><li>Solve Complex Problems: Tackle technical challenges, from optimizing algorithms to deploying real-time machine learning systems. </li></ul> <p>Tech Stack:</p> <ul> <li>Languages: Python, Java </li><li>ML Frameworks: TensorFlow, PyTorch, Scikit-learn </li><li>Databases: BigQuery, PostgreSQL, Redis, MongoDB </li><li>Cloud & DevOps: Kubernetes, Docker, Terraform, GCP, AWS </li><li>MLOps Tools: MLflow, Kubeflow, Airflow </li><li>AI Tools: GitHub Copilot, Cursor AI, Otter AI, ChatGPT </li><li>NLP & LLMs: GPT, BERT, Transformer architectures </li><li>CI/CD & Automation: GitHub Actions, Jenkins </li></ul> <p>What you'll need:</p> <ul> <li>Bachelor's degree in Computer Science, Engineering, or just equivalent hands-on experience with no degree! </li><li>8+ years of hands-on experience in machine learning and full stack development, with at least 3+ years in data science and 3+ years in ML engineering. </li><li>Proven ability to develop and deploy ML models with minimal requirements. </li><li>Strong coding ability demonstrated via a HackerRank assessment (preferred HackerRank Gold Badge). </li><li>Expertise in Python and Java for building production-grade ML systems. </li><li>Strong experience with cloud platforms like GCP, AWS, or Azure. </li><li>Deep understanding of MLOps best practices, including CI/CD pipelines and model monitoring. </li><li>Ability to quickly understand and implement complex data models and algorithms. </li><li>Strong problem-solving, analytical thinking, and debugging skills. </li><li>Excellent communication and collaboration abilities. </li></ul> <p>If you are a highly motivated and results-driven individual, with a passion for driving growth in a fast-paced, entrepreneurial environment, we encourage you to apply for this exciting opportunity. We offer a competitive salary, comprehensive benefits package, and a dynamic work culture that values collaboration, innovation, and personal development.</p>