ML Ops Engineer

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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:

Top Tier Consulting firm

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.