Senior Software Engineer - AI

Color Employer, LLC; Hyderabad; On-site; INR 3,000,000 - 4,200,000; Full time

Job summary

Lyric seeks a skilled Machine Learning Engineer to design, build, and deploy ML models at scale. You will work on end-to-end ML pipelines—from data preprocessing to production deployment—leveraging PyTorch or TensorFlow, Airflow, Kedro, and MLflow. Collaborate with analytics teams to design dashboards for actionable insights.

The role involves distributed data processing with Dask/Spark, data governance, and deploying pipelines to Snowflake/Databricks. 5–7 years of ML experience preferred.

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).
• Strong problem-solving skills and ability to work in agile, fast-paced environments.
• Experience with feature stores and data versioning tools.

Responsibilities

• Train, and optimize ML models using PyTorch or TensorFlow for production-grade applications.
• 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).
• Handle large-scale datasets efficiently using distributed computing frameworks (Dask, Spark).
• Ensure data quality, governance, and deploy pipelines to Snowflake / Databricks.
• Implement model drift detection, performance tracking, and automated retraining strategies.
• Collaborate with data scientists, software engineers, and product teams; document ML workflows.

Skills

Python

Pandas

Dask

NumPy

Scikit-learn

Tools

PyTorch

TensorFlow

Airflow

Kedro

MLflow

DVC

Feast

Tecton

Snowflake

Databricks

Docker

Kubernetes

Azure

AWS

GCP

Job description

Lyric is an AI-first, platform-based healthcare technology company, committed to simplifying the business of care by preventing inaccurate payments and reducing overall waste in the healthcare ecosystem, enabling more efficient use of resources to reduce the cost of care for payers, providers, and patients. Lyric, formerly ClaimsXten, is a market leader with 35 years of pre-pay editing expertise, dedicated teams, and top technology. Lyric is proud to be recognized as 2025 Best in KLAS for Pre-Payment Accuracy and Integrity and is HI-TRUST and SOC2 certified, and a recipient of the 2025 CandE Award for Candidate Experience.

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 pipelines—from 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.

Model Development & Deployment Design

train, and optimize ML models using PyTorch or TensorFlow for production-grade applications.

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.

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).
• · Strong problem-solving skills and ability to work in agile, fast-paced environments.
• · Experience with feature stores (Feast, Tecton) and data versioning tools (DVC).
• · Knowledge of distributed training and GPU optimization.
• · 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, AWS, or GCP) for ML workloads.
• · Contributions to open-source ML projects or technical blogs.

Our ambition is to be an AI-first platform sitting at the intersection of healthcare and fintech, providing simplified consumer and patient solutions to plans and providers in the wake of value-based care while continuously identifying the unmet needs of customers.