AI Developer

Role Overview: As an experienced and results-driven AI Developer, you will be responsible for developing, deploying, and optimizing AI and machine learning models to solve real-world business problems. Your collaboration with cross-functional teams will be crucial in delivering scalable and ethical AI solutions using state-of-the-art tools and technologies. Key Responsibilities: - Collaborate with data scientists and engineers to source, clean, and preprocess large datasets. - Perform feature engineering and data selection to improve model inputs. - Design, build, and validate machine learning and deep learning models, including: - Convolutional Neural Networks (CNNs) - Recurrent Neural Networks (RNNs/LSTMs) - Transformers - NLP and computer vision models - Reinforcement learning agents - Classical ML techniques - Develop models tailored to domain-specific business challenges. - Optimize models for performance, latency, scalability, and resource efficiency. - Ensure models are production-ready for real-time applications. - Build and maintain MLOps pipelines for model deployment, monitoring, and retraining. - Use Docker, Kubernetes, and CI/CD tools for containerization and orchestration. - Deploy models on cloud platforms (AWS, Azure, GCP) or on-premise infrastructure. - Integrate models into systems and applications via APIs or model-serving frameworks. - Implement testing strategies like unit testing, regression testing, and A/B testing. - Continuously improve models based on user feedback and performance metrics. - Stay up to date with AI/ML advancements, tools, and techniques. - Experiment with new approaches to drive innovation and competitive advantage. - Work closely with engineers, product managers, and subject matter experts. - Document model architecture, training processes, and experimental findings. - Communicate complex technical topics to non-technical stakeholders clearly. - Support and implement ethical AI practices focusing on fairness, transparency, and accountability. Qualification Required: - Proficient in Python and experienced with libraries such as TensorFlow, PyTorch, Keras, Scikit-learn. - Solid understanding of ML/DL architectures (CNNs, RNNs/LSTMs, Transformers). - Skilled in data manipulation using Pandas, NumPy, SciPy. - Experience with MLOps tools like MLflow, Kubeflow, DVC. - Familiarity with Docker, Kubernetes, and CI/CD pipelines. - Proven ability to deploy models on cloud platforms (AWS, Azure, or GCP). - Strong foundation in software engineering: Git, unit testing, and code optimization. - Strong analytical mindset with experience working with large datasets. - Excellent communication skills, both written and verbal. - Collaborative team player with experience in agile environments. Additional Company Details: The company values research and innovation, encouraging employees to stay updated with advancements in AI/ML. Preferred qualifications include hands-on experience with LLMs, familiarity with prompt engineering and Retrieval-Augmented Generation (RAG), and understanding of vector databases. Domain-specific experience in sectors like healthcare, finance, retail, manufacturing, or customer service is also beneficial. Academic and research background with publications in top AI/ML conferences (e.g., NeurIPS, ICML, CVPR, ACL) are considered a plus.,