[Urgent] AI/ML Engineer
2 weeks ago
AI/ML Engineer (Remote – Bangalore, India) Location: Bangalore, India (Remote) Contract: 5 months (with potential 12-month extension based on performance) Client: A leading multinational telecommunications company (name disclosed at interview) About the Role We're looking for an experienced AI/ML Engineer to join our advanced analytics and product innovation team. In this role, you'll design, develop, and deploy large-scale machine learning systems for speech, vision, and predictive analytics. You'll collaborate with cross-functional teams across data science, cloud engineering, and product management to drive AI adoption in next-generation telecommunications solutions. Key Responsibilities - Architect and deploy large-scale ML models for speech, vision, and predictive analytics. - Lead research into emerging AI frameworks and tools. - Mentor junior and mid-level engineers, guiding best practices. - Collaborate with product managers and system engineers to integrate ML solutions into products. - Optimize models for scalability, accuracy, and efficiency. Required Skills & Qualifications - Bachelor's or Master's degree in Computer Science, Data Science, Electrical Engineering, or a related discipline. - 3–6 years of experience in machine learning model design, training, and deployment. - Strong programming skills in Python (R a plus). - Proficiency in ML/DL frameworks such as TensorFlow, PyTorch, scikit-learn, and Keras. - Experience with data processing frameworks such as Spark or PySpark. - Strong knowledge of SQL and working with relational databases. - Hands-on experience with Docker, Kubernetes, and CI/CD pipelines for model deployment. - Familiarity with cloud platforms (AWS, Azure, or GCP) and cloud-native ML tools. - Understanding of API development and integration for deploying AI services. - Excellent analytical, problem-solving, and collaboration skills. Preferred Qualifications - Experience with Generative AI or LLM-based systems. - Knowledge of big data tools and distributed computing environments. - Familiarity with observability, model monitoring, and performance tuning. - Exposure to telecommunications or large-scale enterprise AI systems.
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