4seer Technologies

Job Description ML Ops Engineer (2–3 Years Experience) Position Overview We are looking for a passionate and skilled ML Ops Engineer with 2–3 years of experience to join our AI initiatives and services team. The ideal candidate will not only be strong in operationalizing machine learning workflows but also have hands-on exposure or working knowledge in Large Language Models (LLMs), Computer Vision, Speech-to-Text, and Image Recognition to solve real-world problems. This role requires strong adaptability to multiple technologies, excellent communication skills, and a creative problem-solving mindset to bridge the gap between AI research and scalable business solutions. Key Responsibilities Design, build, and maintain end-to-end ML Ops pipelines for deploying and scaling ML/AI models. Automate workflows for model training, deployment, monitoring, and retraining. Ensure scalability, reliability, and performance of ML systems in production. Work with ML Engineers and Data Scientists to bring models from experimentation to production . Manage model versioning, governance, monitoring, and logging . Implement CI/CD for ML workloads with Docker/Kubernetes and cloud platforms (Azure, AWS, or GCP). Support integration of models into client-facing applications and services. Apply ML Ops practices to LLMs, Vision, Speech-to-Text, and Image Recognition use cases . Stay up to date with emerging AI tools and frameworks (LangChain, Hugging Face, OpenAI APIs, etc.) and apply them to real-world problem solving. Required Skills & Qualifications 2–3 years of professional experience in ML Ops / Data Engineering / AI Engineering . Strong programming skills in Python with experience in ML frameworks (TensorFlow, PyTorch, Scikit-learn). Hands-on experience with ML Ops tools (MLflow, Kubeflow, Airflow, DVC, or similar). Solid knowledge of CI/CD pipelines , Docker, Kubernetes, and cloud services (Azure ML, AWS Sagemaker, or GCP Vertex AI). Familiarity with LLMs, Vision, Speech-to-Text, and Image Recognition techniques — practical experience or strong conceptual knowledge. Understanding of prompt engineering, model fine-tuning, or transfer learning is a plus. Excellent problem-solving skills, creativity, and the ability to adapt quickly to new technologies. Strong communication and collaboration skills to work effectively with cross-functional teams and clients. Preferred Skills Exposure to LangChain, Hugging Face Transformers, or RAG-based systems . Experience working with APIs and microservices to serve AI models. Knowledge of monitoring tools for deployed AI models (Prometheus, Grafana, EvidentlyAI). Familiarity with handling unstructured data (text, audio, images, video). Education Bachelor’s or master’s degree in computer science , Data Science, AI/ML, or related field . What We Look For A strong ownership mindset with the ability to deliver end-to-end solutions. Passion for solving real-world business challenges using AI. Professionals who can communicate complex concepts clearly to both technical and non-technical audiences. A creative thinker who stays ahead of trends in AI/ML and ML Ops practices.