Job Description

Role: Lead ML Engineer

Exp: 9+ yrs

Type: Hybrid (One week work from office in a month)

Location: Bangalore/ Chennai

Duration: 6 Months

Timing: 10 AM - 7 PM IST

Responsibilities:

Design, develop, and optimize ML models for NLP, CV, and data-driven applications. Work with transformer architectures, multimodal AI models, and generative AI
techniques.

Implement ML pipelines, including data preprocessing, feature engineering, training,
evaluation, and deployment.

Design and implement a Speech LLM-based virtual assistant Develop and maintain Python code for audio capture, encoding, modality fusion, LLM
interaction, and text output.

Integrate and configure pre-trained models for audio encoding, LLM and audio
decoding.

Stay up-to-date with the latest research and advancements in Speech LLMs, audio
processing, and related fields.

Document the system design, code, and experimental results Architect scalable,
production-ready ML systems integrating MLOps best practices.

Set technical direction, ensuring best practices in AI research, model deployment, and
engineering scalability.

Required Skill:

10+ years of experience in ML, AI, or deep learning, with hands-on experience in NLP,
speech recognition(ASR) and text to speech (TTS)

Knowledge of multimodal learning and techniques for fusing audio and text information. Solid understanding of audio processing concepts, including audio feature extraction,
signal processing, and acoustic modeling.

Experience with fine-tuning transformer models and developing training pipelines. Strong programming skills in Python, with experience in deep learning frameworks such
as PyTorch or TensorFlow

Familiarity with Docker, Kubernetes, Airflow, and serverless computing. Ability to build automated model training, evaluation, and monitoring pipelines.
Job Types: Full-time, Permanent, Contractual / Temporary
Contract length: 12 months

Pay: ₹110,000.00 - ₹130,000.00 per year

Schedule:

Day shift Monday to Friday
Experience:

ML: 10 years (Required)
Work Location: Remote