Machine Learning Engineer

Description:

We are looking for an MLOps Engineer with 3–5 years of experience building and

operating production ML systems, with meaningful exposure to the pharma or healthcare

domain. You will be responsible for taking data science outputs and making them robust,

scalable, and maintainable in production, covering the full lifecycle from pipeline

orchestration and model deployment to monitoring, retraining, and compliance across

client engagements.

You will work closely with data scientists, data engineers, and business stakeholders to

bridge the gap between experimental models and production-grade systems.

Experience navigating regulated or DG-sensitive environments is a strong plus.

Key Responsibilities:

• Own and operate end-to-end ML pipelines from feature engineering and training

runs through deployment, monitoring, and scheduled retraining, on cloud

infrastructure (AWS, Azure, or GCP).

• Implement and maintain CI/CD pipelines for ML workflows, ensuring

reproducibility, version control of models and data, and reliable rollback

capabilities.

• Build and orchestrate ML and data pipelines using tools like Airflow, Prefect, or

similar; manage dependencies, scheduling, and failure handling.

• Build, manage, and govern ML workflows on Databricks, including Jobs, Delta Live

Tables, and Unity Catalog, as the primary platform for data and ML pipeline

execution.

• Manage the full model lifecycle using MLflow on Databricks: experiment tracking,

model registry, versioning, stage transitions, and lineage documentation.

• Design and deploy ML pipelines on Kubernetes using Kubeflow Pipelines for clients

who operate outside Databricks environments, including pipeline authoring,

component containerization, and run management.

• Leverage pre-trained models, foundation models (LLMs), and cloud AI services

instead of building models from scratch where appropriate; implement RAG

pipelines, prompt engineering workflows, and evaluation frameworks.

• Containerize and deploy models as scalable APIs or batch inference services

using Docker and Kubernetes (or managed equivalents); define SLAs and monitor

adherence.

• Instrument production models for performance drift, data quality degradation,

and upstream schema changes; define alerting and intervention protocols.

• Collaborate with data scientists to translate experimental notebooks into

maintainable, testable, production-ready code.

• Maintain experiment tracking and model registries (MLflow or equivalent); enforce

model lineage and reproducibility standards.

• Partner with data governance and compliance teams to ensure ML pipelines meet

audit, traceability, and access-control requirements relevant to

pharma/healthcare data.

• Participate in code reviews, architecture discussions, and documentation, raising

the engineering bar across the DS/ML team.

• Develop visualization layers and support self-service analytics platforms.

Required Skills & Qualifications:

• 3–5 years of hands-on MLOps or ML engineering experience in a production

environment.

• Bachelors or masters

• Strong proficiency in Python with experience in packaging, testing, and generating

production-grade code. Along with comfortability in handling complex querying

across big data through SQL/PySpark

• Hands-on experience in Databricks with Jobs, Delta Lake, Unity Catalog, and the

Databricks ML runtime beyond Spark compute. Additionally experience with

MLflow on Databricks for experiment tracking, model registry (including Unity

Catalog-backed registry), lifecycle management, and model serving.

• Docker, Kubernetes or equivalents for containerization and scalable model

serving.

• Experience authoring, deploying, and managing ML pipelines on Kubeflow;

comfortable working within existing Kubernetes clusters on client infrastructure.

• Design and implement agent-based or orchestration-driven AI workflows using

frameworks such as LangChain, LangGraph, or cloud-native equivalents.

• CI/CD pipelines for ML workflows (GitHub Actions, Azure DevOps, or equivalent).

• REST API design and deployment of model-serving endpoints.

• Experience with Cloud platforms (AWS, Azure, or GCP) for infrastructure

management and managed ML services.

• Model monitoring, drift detection, and alerting in production.

• Git and version control best practices; Linux and Bash scripting.

• Hands-on experience with US pharma data sources like claims, EHR, Rx, or similar.

Nice to have

• SageMaker, Vertex AI, or other managed ML platforms.

• OpenShift or other enterprise Kubernetes distributions commonly found in pharma

client environments.

• Sufficient depth of understanding with scikit-learn, XGBoost, PyTorch, or

TensorFlow, to debug and optimize what data scientists hand over.

• Terraform or IaC tooling for ML infrastructure.

• OMOP, FHIR, or HL7 familiarity.

• Awareness of 21 CFR Part 11, GxP, or audit trail requirements in pharma ML contexts.

• LLM/GenAI pipeline experience: prompt versioning, evaluation, latency monitoring