AI Engineer

Accountabilities: Develop machine learning models: Assist in designing, building, training, and fine-tuning machine learning models to address business challenges and support analytical products. Prepare and process data: Work with large datasets to perform data cleaning, preprocessing, feature engineering, and training-data preparation, ensuring data quality and suitability for model development. Implement deep learning solutions: Build, test, and optimize machine learning and deep learning models using frameworks such as PyTorch, TensorFlow, and Scikit-learn. Develop NLP and LLM applications: Support research and implementation of artificial intelligence solutions involving natural language processing, large language models, text analysis, and predictive analytics. Evaluate model performance: Design and conduct experiments, analyze results, and apply appropriate evaluation metrics to assess model accuracy, reliability, efficiency, and overall effectiveness. Optimize AI systems: Improve model performance by evaluating computational efficiency, inference latency, resource utilization, and other relevant performance indicators. Integrate models into production: Collaborate with software engineers to deploy AI models and integrate them into production environments, including applications and API-based services. Apply AI development tools: Use relevant libraries, frameworks, and cloud-based AI services to streamline experimentation, development, deployment, and model management. Support MLOps practices: Contribute to reproducible model development, testing, deployment workflows, and continuous integration and delivery practices for machine learning systems. Follow AI advancements: Stay informed about emerging machine learning techniques, deep learning architectures, LLM capabilities, and industry developments, applying relevant innovations to ongoing projects. Promote responsible AI: Consider ethical AI principles, potential bias, transparency, and model interpretability when developing and evaluating machine learning solutions. Collaborate across teams: Work with technical colleagues to clarify requirements, share findings, troubleshoot issues, and deliver AI solutions aligned with business goals. Requirements A bachelor's or master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical discipline. 3–4 years of relevant hands-on experience in AI engineering, machine learning, data science, or a closely related field. A solid understanding of machine learning algorithms, deep learning fundamentals, and common model development techniques. Practical programming experience in Python, including the ability to develop, test, and maintain machine learning workflows. Hands-on experience with at least one major AI or machine learning framework, such as PyTorch or TensorFlow. Familiarity with Scikit-learn and data processing libraries such as Pandas and NumPy, as well as regular expressions for text and data processing. Experience with model training, fine-tuning, experimentation, and performance optimization. Understanding of model evaluation methodologies, performance metrics, and techniques for assessing model accuracy and effectiveness. Familiarity with natural language processing (NLP) and large language model (LLM) applications. Ability to prepare and transform datasets through data cleaning, preprocessing, and feature engineering. Strong analytical and problem-solving skills, with the ability to interpret experimental results and identify opportunities for improvement. Good collaboration and communication skills, including the ability to work with software engineers and other technical stakeholders to deliver production-ready solutions. A proactive approach to learning, experimentation, technical problem-solving, and keeping up with developments in AI and machine learning. Additional experience that would be an advantage: Experience with NLP and LLM frameworks such as Hugging Face Transformers and spaCy. Exposure to cloud-based AI services, including AWS SageMaker, Google Vertex AI, or Azure AI. Knowledge of MLOps practices, including model deployment, automated testing, and CI/CD workflows for machine learning systems. Familiarity with AI ethics, bias detection, responsible AI practices, and model interpretability. Experience integrating machine learning models into applications or API-based services. Benefits Fully remote work: Work remotely from India, with no regular office attendance required. Competitive compensation: A competitive salary and comprehensive benefits package; specific salary figures are not provided in the source description. Paid vacation: Time off to rest, recharge, and maintain work-life balance. Parental leave: Support for employees and their families during important life transitions. Learning reimbursement: Financial support for professional learning and skill development. Career growth: Opportunities to take ownership of impactful projects and develop technical expertise through practical experience. Continuous learning: An environment that encourages experimentation, skill mastery, and staying current with advances in AI and machine learning. High-impact work: The opportunity to develop AI capabilities that support research, analytics, and data-driven decisions for major enterprise customers. Ownership and autonomy: Responsibility for meaningful projects, with growth driven by contributions and results rather than tenure alone. Collaborative culture: Work with globally distributed teams in an environment that values transparency, mutual respect, and continuous improvement. Inclusive workplace: Commitment to equal employment opportunity and a diverse workforce where people can contribute and grow. Defined working hours: Standard working hours are 8:00 a.m. to 5:00 p.m. India Standard Time (IST).