Senior Software Engineer

Creditas Solutions

3 - 5 years

5 - 10 Lacs

gurugram

Posted:1 day ago| Platform:

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Skills Required

jax kubernetes cd python signal processing microsoft azure machine learning backend development docker deep learning tensorflow apache spark gcp kafka pytorch debugging software engineering api aws programming communication skills ml

Work Mode

Work from Office

Job Type

Full Time

Job Description

Creditas is focusing on operationalizing voicebots and audio analytics platforms, with a strong emphasis on ML models, LLM models, and embedding models, targeting experience of 3-5 years

Role and Responsibilities:

Lead design, development, and operationalization of workflows for voicebots and audio analytics platforms
Build, deploy, and maintain scalable machine learning models, including LLMs (Large Language Models) and embedding models
Develop infrastructure for continuous data processing and feature engineering relevant to voice and audio data
Collaborate with cross-functional teams to integrate ML models into production systems ensuring high performance and reliability
Conduct systematic feature engineering including aggregation, embedding, and application of deep learning techniques
Drive improvements in voice recognition accuracy, audio signal processing, and real-time analytics
Implement monitoring, debugging, and optimization routines for ML applications in voicebots
Champion best practices in CI/CD for model deployment, including automated testing and performance evaluation
Mentor junior engineers and contribute to team knowledge sharing
Preferred Skills and Experience: 3-5 years of software engineering experience with a focus on machine learning and audio/voice technologies
Hands-on knowledge of ML frameworks such as TensorFlow, PyTorch, or JAX
Experience working with LLMs and embedding models applied to speech and audio data
Proficiency in Python and other programming languages used in ML and backend development
Understanding of distributed systems, cloud platforms (AWS, GCP, Azure), and containerization tools like Docker and Kubernetes
Familiarity with backend technologies, APIs, large-scale data processing (Apache Spark, Kafka), and data storage solutions
Experience in operationalizing and scaling ML models in production environments
Strong problem-solving, communication, and collaboration skills

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