Machine Learning Engineer

As a Machine Learning Engineer at Tecblic, you will be an integral part of a forward-thinking team that specializes in delivering AI-driven solutions to empower businesses in the digital age. Your passion for LLMs, Computer Vision, MLOps, and Agentic AI will drive you to excel in the following responsibilities: - **Research and Development:** Design, develop, and fine-tune machine learning models across LLM, computer vision, and Agentic AI use cases. - **Model Optimization:** Fine-tune and optimize pre-trained models to ensure performance, scalability, and minimal latency. - **Computer Vision:** Build and deploy vision models for object detection, classification, OCR, and segmentation. - **Integration:** Collaborate closely with software and product teams to integrate models into production-ready applications. - **Data Engineering:** Develop robust data pipelines for structured, unstructured (text/image/video), and streaming data. - **Production Deployment:** Deploy, monitor, and manage ML models in production using DevOps and MLOps practices. - **Experimentation:** Prototype and test new AI approaches such as reinforcement learning, few-shot learning, and generative AI. - **DevOps Collaboration:** Work hand in hand with the DevOps team to ensure CI/CD pipelines, infrastructure-as-code, and scalable deployments are in place. - **Technical Mentorship:** Provide support and mentorship to junior ML and data engineers. Your core technical skills will include: - Strong Python skills for machine learning and computer vision. - Hands-on experience with PyTorch, TensorFlow, Hugging Face, Scikit-learn, OpenCV. - Deep understanding of LLMs (e.g., GPT, BERT, T5) and Computer Vision architectures (e.g., CNNs, Vision Transformers, YOLO, R-CNN). - Strong knowledge of NLP tasks, image/video processing, and real-time inference. - Experience in cloud platforms: AWS, GCP, or Azure. - Familiarity with Docker, Kubernetes, and serverless deployments. - Proficiency in SQL, Pandas, NumPy, and data wrangling. Your MLOps Skills will encompass: - Experience with CI/CD tools such as GitHub Actions, GitLab CI, Jenkins, etc. - Knowledge of Infrastructure as Code (IaC) tools like Terraform, CloudFormation, or Pulumi. - Familiarity with container orchestration and Kubernetes-based ML model deployment. - Hands-on experience with ML pipelines and monitoring tools: MLflow, Kubeflow, TFX, or Seldon. - Understanding of model versioning, model registry, and automated testing/validation in ML workflows. - Exposure to observability and logging frameworks (e.g., Prometheus, Grafana, ELK). Additional Skills (Good to Have) include: - Knowledge of Agentic AI systems and use cases. - Experience with generative models (e.g., GANs, VAEs) and RL-based architectures. - Prompt engineering and fine-tuning for LLMs in specialized domains. - Working with vector databases (e.g., Pinecone, FAISS, Weaviate). - Distributed data processing using Apache Spark. Your profile should reflect: - Strong foundation in mathematics, including linear algebra, probability, and statistics. - Deep understanding of data structures and algorithms. - Comfortable handling large-scale datasets, including images, video, and multi-modal. - Strong analytical and problem-solving mindset. - Excellent communication skills for cross-functional collaboration. - Self-motivated, adaptive, and committed to continuous learning.,