MLOps Architect

Senior MLOps Architect

Lead ML Engineer

MLflow

Feast

Key Responsibilities

1. MLOps Architecture & Strategy

   • Drive the design and architecture of advanced MLOps frameworks, ensuring seamless integration between data ingestion, model training, deployment, and monitoring.
   • Evaluate, select, and implement appropriate MLOps tools and platforms (MLflow, Feast, etc.) to standardize processes and accelerate ML deployments.
   • Establish best practices for infrastructure, security, and governance that meet enterprise-grade requirements.

2. ML Pipeline Development & Automation

   • Design and maintain CI/CD pipelines that facilitate automated model building, testing, and deployment.
   • Leverage

     MLflow

     for experiment tracking, model versioning, and reproducible pipelines.
   • Integrate

     Feast

     Feature Store to streamline feature engineering, management, and versioning across different ML models.

3. Model Serving & Monitoring

   • Implement robust, low-latency model serving solutions (e.g., using Docker/Kubernetes, REST APIs, or specialized serving frameworks).
   • Set up comprehensive model monitoring systems to track performance metrics, model drift, and data quality in production.
   • Establish alerting mechanisms and feedback loops for continuous model improvements and proactive issue resolution.

4. Data & Feature Management

   • Collaborate with Data Engineering teams to ensure efficient data pipelines, data governance, and data quality.
   • Architect and maintain the

     Feast

     Feature Store for consistent, reusable, and high-quality features.
   • Develop strategies for feature lifecycle management, including feature discovery, validation, and retirement.

5. Technical Leadership & Mentorship

   • Guide and mentor a team of ML engineers and data scientists, promoting a culture of knowledge sharing and innovation.
   • Conduct technical reviews, provide feedback, and ensure adherence to coding standards, design principles, and best practices.
   • Collaborate with stakeholders (product managers, DevOps, IT) to align ML solutions with business objectives and technical feasibility.

6. Research & Innovation

   • Stay updated on emerging trends and technologies in MLOps, cloud-native solutions, and AI frameworks.
   • Evaluate new tools, libraries, and methodologies, incorporating the most effective ones into the development lifecycle.
   • Advocate for continuous improvement and experimentation within the ML engineering function.

7. Documentation & Compliance

   • Produce and maintain detailed architectural diagrams, system configurations, and operational runbooks.
   • Ensure compliance with data privacy, security, and regulatory requirements throughout the ML lifecycle.
   • Support audit and compliance requests by maintaining clear, consistent documentation of processes and systems.

Required Qualifications

• Overall Experience

  : 14+ years in software engineering, data engineering, or data science roles.

• Relevant MLOps Experience

  : 4-5 years of hands-on experience designing and implementing MLOps frameworks.

• Technical Expertise

  :

  • MLflow

    : Proficient with experiment tracking, model registry, and model packaging.

  • Feast Feature Store

    : Demonstrated experience implementing and managing feature stores at scale.

  • Model Serving

    : In-depth understanding of deployment strategies (batch/streaming/real-time) using Docker, Kubernetes, or similar tools.

  • Model Monitoring

    : Experience setting up performance metrics, alerting, and drift detection.

  • Python

    : Extensive coding experience (data manipulation, building APIs, scripting).

  • Cloud & DevOps

    : Familiarity with AWS, Azure, or GCP services, CI/CD tools (Jenkins, GitLab CI, etc.), and infrastructure-as-code (Terraform, CloudFormation).

• Soft Skills

  :
  • Strong communication skills to articulate complex technical solutions to various stakeholders.
  • Leadership and team-building capabilities, with a track record of mentoring engineers and data scientists.
  • Problem-solving mindset with the ability to handle ambiguity and drive results in a fast-paced environment.

Preferred / Bonus Skills

• Experience with

  distributed data processing

  frameworks like Spark or Hadoop.
• Knowledge of

  container orchestration

  and service mesh technologies (e.g., Istio, Envoy).
• Familiarity with

  feature engineering

  methodologies, advanced ML/DL frameworks (TensorFlow, PyTorch), or specialized libraries for NLP or computer vision.
• Exposure to

  big data

  tools, streaming data platforms (Kafka), or real-time analytics solutions.