AI/ML Engineer

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

1. Architect and deploy large-scale ML models for speech, vision, and predictive analytics.
2. Lead research into emerging AI frameworks and tools.
3. Mentor junior and mid-level engineers, guiding best practices.
4. Collaborate with product managers and system engineers to integrate ML solutions into products.
5. 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.