ML Ops Engineer

To expedite your application, please use this link: https://tally.so/r/mBqrre

About Us:

Soul AI is a pioneering company founded by IIT Bombay and IIM Ahmedabad alumni, with a strong founding team from IITs, NITs, and BITS. We specialize in delivering high-quality human-curated data and AI-first scaled operations services. Based in San Francisco and Hyderabad, we are a fast-moving team on a mission to build AI for Good, driving innovation and societal impact.

Role Overview:

We are seeking a skilled MLOps Engineer to join our clients project (Top Tier Consulting firm) and operationalize our machine learning workflows. You will work closely with data scientists, engineers, and product teams to design, deploy, monitor, and maintain scalable ML pipelines in production. The ideal candidate has a strong foundation in ML systems, DevOps principles, and cloud-native technologies.

Responsibilities:

• Design and build end-to-end ML pipelines including data ingestion, training, evaluation, deployment, and monitoring.
• Develop and manage CI/CD workflows for ML models.
• Build robust model serving solutions (REST/gRPC APIs, batch, streaming).
• Collaborate with data scientists to productionize notebooks and experiments into reproducible, scalable systems.
• Implement model versioning, experiment tracking, and performance monitoring tools.
• Automate infrastructure provisioning using IaC tools like Terraform or CloudFormation.
• Ensure compliance with data governance, privacy, and security standards.
• Proactively monitor model drift, data skew, and system failures in production environments.
• Enable real-time and batch inference pipelines as per use case needs.

Required Skills:

• Solid programming skills in Python; familiarity with ML frameworks (PyTorch, TensorFlow, Scikit-learn).
• Experience with Docker, Kubernetes, and container orchestration in production environments.
• Strong knowledge of CI/CD tools (GitHub Actions, Jenkins, GitLab CI) tailored to ML workflows.
• Experience with orchestration tools like Apache Airflow, Prefect, or Kubeflow Pipelines.
• Hands-on experience with AWS cloud platform
• Familiarity with model tracking and experiment management (MLflow, Weights & Biases).
• Proficiency in logging/monitoring tools (Prometheus, Grafana, ELK.).
• Understanding of data versioning and reproducibility tools like DVC or LakeFS.
• Experience building and scaling ML workflows on cloud platforms like AWS (S3, SageMaker, Lambda)
• Experience with PySpark and distributed data processing
• Familiarity with data pipeline orchestration (Airflow, Databricks workflows, etc.)

Nice to Have:

• Experience with feature stores (Feast, Tecton) and model registries.
• Exposure to real-time inference using Kafka, Redis, or serverless architectures.
• Knowledge of explainability and fairness tools (SHAP, LIME, Fairlearn).
• Familiarity with data observability platforms (WhyLabs, Arize AI).
• Background in data engineering or backend development is a plus.

Educational Qualifications:

• Bachelors or Masters degree in Computer Science, Engineering, Data Science, or a related field.
• Certifications in cloud platforms or ML engineering are a bonus.