Senior Software Engineer ( AI/ML Developer )

Lyric

Hyderabad

On-site

INR 1,800,000 - 3,000,000

Full time

4 days ago

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Job summary

Lyric is seeking a highly skilled ML Engineer to design, build, and deploy ML models at scale. You will own end-to-end pipelines from data preprocessing to production deployment, leveraging modern frameworks and MLOps practices.

This role emphasizes robust model development and scalable data workflows. Collaborate with analytics teams to design dashboards and monitor model performance, ensuring governance and reproducibility across environments.

Qualifications

5–7 years of experience in ML engineering or applied ML.
Strong proficiency in Python and libraries like Pandas, Dask, NumPy, Scikit-learn.
Hands-on experience with PyTorch or TensorFlow for model development.
Solid understanding of MLOps tools: Airflow, Kedro, MLflow (or equivalents).
Experience deploying ML models in production environments (APIs, batch jobs, streaming).
Hands-on experience with big-data and lakehouse platforms such as Apache Spark, Databricks, and Snowflake for large-scale data processing and ML pipelines.

Responsibilities

Design, train, and optimize ML models for production-grade applications.
Build scalable data pipelines for feature engineering and model training.
Implement model evaluation, hyperparameter tuning, and performance monitoring.
Develop ML workflows using Airflow, Kedro, and MLflow for reproducibility.
Automate model deployment and lifecycle management across environments.
Collaborate with data scientists, engineers, and product teams to align ML solutions with business goals.
Document ML workflows and best practices.

Skills

Strong problem-solving skills

Agile environment experience

Tools

Python

Pandas

Dask

NumPy

Scikit-learn

PyTorch

TensorFlow

Airflow

Kedro

MLflow

Spark

Databricks

Snowflake

Weights & Biases

Feast

Tecton

Docker

Kubernetes

Power BI

Job description

JOB SUMMARY

We are looking for a highly skilled Machine Learning Engineer with hands-on experience in designing, building, and deploying ML models at scale. You will work on end-to-end ML pipelinesfrom data preprocessing to production deployment—leveraging modern frameworks and MLOps practices. This role is ideal for someone who thrives in solving complex problems, optimizing workflows, and applying AI to deliver impactful business solutions. Additionally, you will collaborate with analytics teams to design dashboards and visualizations that provide actionable insights for stakeholders. This is a pure Machine Learning role: candidates will be evaluated on core ML engineering competencies—model development, data pipelines, and MLOps—rather than on Generative AI experience.

Design, train, and optimize ML models using PyTorch or TensorFlow for production-grade applications.
Build scalable data pipelines for feature engineering and model training using Pandas, Dask, or equivalent frameworks.
Implement model evaluation, hyperparameter tuning, and performance monitoring.

MLOps

Develop and maintain ML workflows using Airflow, Kedro, and MLflow for reproducibility and traceability.
Automate model deployment and lifecycle management across environments (dev, staging, production).

Data Engineering & Processing

Handle large-scale datasets efficiently using distributed computing frameworks (Dask, Spark).
Ensure data quality, consistency, and compliance with governance standards.
Work on and deploy pipelines to Snowflake / Databricks.

Monitoring & Observability

Implement model drift detection, performance tracking, and automated retraining strategies.
Use experiment tracking tools (MLflow, Weights & Biases) for transparency and reproducibility.

Collaboration & Documentation

Work closely with data scientists, software engineers, and product teams to align ML solutions with business goals.
Document ML workflows, best practices, and operational guidelines.

ESSENTIAL JOB RESPONSIBILITIES & KEY PERFORMANCE OUTCOMES

Model Development & Deployment

Design, train, and optimize ML models using PyTorch or TensorFlow for production-grade applications.
Build scalable data pipelines for feature engineering and model training using Pandas, Dask, or equivalent frameworks.
Implement model evaluation, hyperparameter tuning, and performance monitoring.

MLOps

Develop and maintain ML workflows using Airflow, Kedro, and MLflow for reproducibility and traceability.
Automate model deployment and lifecycle management across environments (dev, staging, production).

Data Engineering & Processing

Handle large-scale datasets efficiently using distributed computing frameworks (Dask, Spark).
Ensure data quality, consistency, and compliance with governance standards.
Work on and deploy pipelines to Snowflake / Databricks.

Monitoring & Observability

Implement model drift detection, performance tracking, and automated retraining strategies.
Use experiment tracking tools (MLflow, Weights & Biases) for transparency and reproducibility.

Collaboration & Documentation

Work closely with data scientists, software engineers, and product teams to align ML solutions with business goals.
Document ML workflows, best practices, and operational guidelines.

REQUIRED QUALIFICATIONS

5–7 years of experience in ML engineering or applied machine learning.
Strong proficiency in Python and libraries like Pandas, Dask, NumPy, Scikit-learn.
Hands-on experience with PyTorch or TensorFlow for model development.
Solid understanding of MLOps tools: Airflow, Kedro, MLflow (or equivalents).
Experience deploying ML models in production environments (APIs, batch jobs, streaming).
Hands-on experience with big-data and lakehouse platforms such as Apache Spark, Databricks, and Snowflake for large-scale data processing and ML pipelines.
Strong problem-solving skills and ability to work in agile, fast-paced environments.

PREFERRED QUALIFICATIONS