Machine Learning (ML) Engineer
Expertshub.ai ·
- Work mode
- Onsite
- Category
- ML ai
- Experience
- 5+ years
Expertshub.ai ·
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The ML Engineer integrates AI/ML models into production, optimizes inference pipelines, manages CI/CD workflows, and builds Agentic/multimodal AI systems using Python, PyTorch/TensorFlow, Docker, Kubernetes, and cloud platforms.
Job Overview
The ML Engineer will focus on integrating AI models into production environments, optimizing for scalability and performance, and managing CI/CD workflows. The role also involves working with Agentic frameworks (LangGraph, Ango), developing pipelines that interact with language models, and contributing to multimodal AI systems (text, vision, and speech). The engineer will collaborate with AI Researchers, Data Engineers, and Application Developers to deploy robust, efficient AI solutions.
Key Responsibilities ________________________________________
• Integrate and deploy AI/ML models into production environments using scalable, automated workflows.
• Optimize model inference pipelines for performance, scalability, and reliability.
• Manage CI/CD systems for continuous deployment and testing of ML components.
• Implement and maintain Agentic workflows using frameworks such as LangGraph, Ango,RAG
• Collaborate with the AI Research team on Transformer-based architectures and NLP model integration.
• Understanding on multimodal AI features (vision, text-to-voice, and audio-based applications).
Required Skills & Qualifications ________________________________________
• 1–1.5 years of experience as an ML Engineer or equivalent role in AI/ML deployment.
• Proficiency in Python and libraries such as TensorFlow, PyTorch, and Scikit-learn Pytorch
• Hands-on experience with FastAPI or Flask for serving models.
• Strong understanding of NLP concepts, Transformer models, and fine-tuning workflows.
• Experience managing CI/CD pipelines (GitHub Actions, Jenkins, or similar).
• Familiarity with Agentic workflow orchestration tools (LangGraph, TLangGraph, Ango).
• Knowledge of Docker, Kubernetes, and cloud deployment (AWS/GCP/Azure).
Preferred (Good to Have) ________________________________________
• Exposure to LangSmith for model tracing and performance evaluation.
• Experience working with databases, computer vision, and text-to-voice integration.
• Knowledge of multimodal systems combining text, vision, and audio processing.
• Familiarity with MLOps and model lifecycle management tools (MLflow, DVC).
Soft Skills ________________________________________
• Strong problem-solving and debugging mindset.
• Ability to collaborate effectively with AI Research and Application teams.
• Clear communication and technical documentation skills.