Artificial Intelligence Developer

As an experienced and results-driven AI Developer, your role will involve developing, deploying, and optimizing AI and machine learning models to address real-world business challenges. You will collaborate with cross-functional teams to deliver scalable and ethical AI solutions utilizing cutting-edge tools and technologies. - Data Engineering & Preprocessing - Collaborate with data scientists and engineers to source, clean, and preprocess large datasets. - Perform feature engineering and data selection to enhance model inputs. - AI Model Development & Implementation - Design, build, and validate various 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 address domain-specific business challenges. - Performance Optimization & Scalability - Optimize models for performance, latency, scalability, and resource efficiency. - Ensure models are production-ready for real-time applications. - Deployment, MLOps & Integration - Build and maintain MLOps pipelines for model deployment, monitoring, and retraining. - Utilize 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. - Testing, Validation & Continuous Improvement - Implement testing strategies like unit testing, regression testing, and A/B testing. - Continuously enhance models based on user feedback and performance metrics. - Research & Innovation - Stay updated with AI/ML advancements, tools, and techniques. - Experiment with new approaches to drive innovation and maintain a competitive edge. - Collaboration & Communication - Work closely with engineers, product managers, and subject matter experts. - Document model architecture, training processes, and experimental findings. - Communicate complex technical topics clearly to non-technical stakeholders. - Ethical AI Practices - Support and implement ethical AI practices focusing on fairness, transparency, and accountability. **Required Qualifications & Skills** - Core Technical Skills - Proficiency in Python and experience with libraries like TensorFlow, PyTorch, Keras, Scikit-learn. - Solid understanding of ML/DL architectures (CNNs, RNNs/LSTMs, Transformers). - Skills in data manipulation using Pandas, NumPy, SciPy. - MLOps & Deployment Experience - Experience with MLOps tools such as MLflow, Kubeflow, DVC. - Familiarity with Docker, Kubernetes, and CI/CD pipelines. - Proven ability to deploy models on cloud platforms (AWS, Azure, or GCP). - Software Engineering & Analytical Thinking - Strong foundation in software engineering: Git, unit testing, and code optimization. - Strong analytical mindset with experience working with large datasets. - Communication & Teamwork - Excellent communication skills, both written and verbal. - Collaborative team player with experience in agile environments. **Preferred** - Advanced AI & LLM Expertise - Hands-on experience with LLMs (e.g., GPT, Claude, Mistral, LLaMA). - Familiarity with prompt engineering and Retrieval-Augmented Generation (RAG). - Experience with LangChain, LlamaIndex, and Hugging Face Transformers. - Understanding of vector databases (e.g., Pinecone, FAISS, Weaviate). - Domain-Specific Experience - Experience applying AI in sectors like healthcare, finance, retail, manufacturing, or customer service. - Specialized knowledge in NLP, computer vision, or reinforcement learning. - Academic & Research Background - Strong background in statistics and optimization. - Research publications in top AI/ML conferences (e.g., NeurIPS, ICML, CVPR, ACL) are a plus.,