Machine Learning Engineer

As a Machine Learning Engineer at Tecblic, you will be part of a forward-thinking team that specializes in delivering AI-driven solutions to empower businesses in the digital age. Your primary responsibilities will include: - 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 for improved performance, scalability, and minimal latency. - Computer Vision: Build and deploy vision models for tasks such as 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 like reinforcement learning, few-shot learning, and generative AI. - DevOps Collaboration: Work with the DevOps team to ensure CI/CD pipelines, infrastructure-as-code, and scalable deployments are in place. - Technical Mentorship: Support and mentor junior ML and data professionals. Your core technical skills should include: - Strong Python skills for machine learning and computer vision. - Hands-on experience with PyTorch, TensorFlow, Hugging Face, Scikit-learn, and OpenCV. - Deep understanding of LLMs (e.g., GPT, BERT, T5) and Computer Vision architectures (e.g., CNNs, Vision Transformers, YOLO, R-CNN). - Proficiency in NLP tasks, image/video processing, real-time inference, and cloud platforms like AWS, GCP, or Azure. - Familiarity with Docker, Kubernetes, serverless deployments, SQL, Pandas, NumPy, and data wrangling. Your MLOps Skills should include: - Experience with CI/CD tools like GitHub Actions, GitLab CI, Jenkins. - Knowledge of Infrastructure as Code (IaC) tools such as Terraform, CloudFormation, or Pulumi. - Familiarity with container orchestration, Kubernetes-based ML model deployment, and hands-on experience with ML pipelines and monitoring tools like MLflow, Kubeflow, TFX, or Seldon. - Understanding of model versioning, model registry, automated testing/validation in ML workflows, observability, and logging frameworks (e.g., Prometheus, Grafana, ELK). Additionally, Good to Have Skills: - 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), and distributed data processing using Apache Spark. Your profile should also include: - A strong foundation in mathematics, including linear algebra, probability, and statistics. - Deep understanding of data structures and algorithms, comfortable handling large-scale datasets, and strong analytical and problem-solving mindset. - Excellent communication skills for cross-functional collaboration, self-motivation, adaptability, and a commitment to continuous learning. Join Tecblic to innovate with cutting-edge technology in the exciting realm of AI and machine learning!,