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, rapid-paced environments.

PREFERRED QUALIFICATIONS

- Experience with feature stores (Feast, Tecton) and data versioning tools (DVC).
- Experience with Power BI or similar BI tools for analytics and visualization.
- Understanding of model explainability and responsible AI practices.
- Familiarity with containerization (Docker) and orchestration (Kubernetes).
- Exposure to cloud platforms (Azure or AWS) for ML workloads.
- Contributions to open-source ML projects or technical blogs.

📌 Senior Software Engineer ( AI/ML Developer ) (Hyderabad)
🏢 Lyric
📍 Hyderabad