AI-ML Tech Engineer

Job Overview

We are seeking a talented AI-ML Tech Engineer with a robust foundation in machine learning, data science, and software engineering. The ideal candidate will possess proven experience in developing and deploying sophisticated machine learning models, effectively managing large datasets, and fostering collaboration within cross-functional teams to address complex, real-world challenges.

Key Responsibilities

• Develop and deploy advanced machine learning and Natural Language Processing (NLP) solutions utilizing prevalent ML libraries and frameworks.
• Process and analyze large datasets within distributed computing environments to derive actionable insights.
• Perform fine-tuning of deep learning models, including cutting-edge Large Language Models (LLMs) and Small Language Models (SLMs).
• Implement and optimize robust ML pipelines for the entire lifecycle: training, evaluation, and inference.
• Leverage cloud-based AI services for streamlined model deployment and efficient management.
• Establish and maintain Continuous Integration and Continuous Deployment (CI/CD) pipelines tailored for ML training and deployment workflows.
• Engage with AI-powered solutions that incorporate Vector Stores and Retrieval-Augmented Generation (RAG) pipelines.
• Articulate and translate ML-derived outcomes into clear, business-digestible insights for stakeholders.
• Collaborate closely with stakeholders to precisely define project requirements and deliver innovative AI solutions that drive business value.

Mandatory Skills & Qualifications

• Expert proficiency in Python and leading ML frameworks such as TensorFlow, PyTorch, Keras, and Scikit-learn.
• Strong theoretical and practical understanding of statistical models, regression, clustering, and fundamental ML algorithms (e.g., decision trees, Random Forests, neural networks).
• Demonstrated experience in successfully deploying ML models into production environments.
• Proficiency with major cloud AI services, including AWS, Azure, or GCP.
• In-depth knowledge of large language models like OpenAI's GPT-3.5, GPT-4, and Codex.
• Hands-on experience working with vector databases and implementing RAG-based ML solutions.
• Expertise in various ML deployment strategies, encompassing both static and dynamic approaches.
• Significant experience in MLOps, automation, and setting up CI/CD pipelines specifically for ML workflows.
• Exceptional communication abilities and a proven track record in managing stakeholder expectations and interactions.

Nice-to-Have Skills

• Experience with specialized MLOps tools designed for continuous integration and deployment processes.
• Familiarity with full-stack development principles and experience with API frameworks like Flask or Django.
• Exposure to data engineering concepts including Databricks, Data Mesh architectures, and ETL pipelines.
• Practical experience in containerizing applications using Docker.
• Familiarity with agile development methodologies such as Scrum or Kanban, and proficiency with project management tools like JIRA/GitLab.