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We are an IT Solutions Integrator/Consulting Firm helping our clients hire the right professional for an exciting long-term project. Here are a few details.

Requirements

Key
Responsibilities

ML CI/CD
& Deployment

• 
  Design, build, and maintain
  CI/CD pipelines for
  Machine Learning workflows
  , including:
• 
  Model training
• 
  Model validation
• 
  Model packaging
• 
  Model deployment
• 
  Ensure ML pipelines operate efficiently across
  development,
  testing, and production environments
  .

Model
Deployment & Serving

• 
  Implement and manage
  model deployment patterns
  ,
  including:
• 
  Batch inference
• 
  Real-time inference
• 
  Streaming inference
• 
  Develop and maintain
  model serving
  infrastructure
  for scalable and reliable ML inference.

Model
Observability & Monitoring

• 
  Establish comprehensive
  model observability
  frameworks
  to monitor:
• 
  Data drift
• 
  Model performance degradation
• 
  Latency
• 
  System failures
• 
  Bias and quality signals

Feature
Engineering Infrastructure

• 
  Build and manage
  feature pipelines and feature
  stores
  .
• 
  Ensure
  data lineage, reproducibility, and
  traceability
  across ML workflows.

Experiment
Management & Model Governance

• 
  Operationalize
  experiment tracking frameworks
  .
• 
  Manage
  model registry and artifact management
  systems
  , including:
• 
  Versioning of
  code
• 
  Versioning of
  datasets
• 
  Versioning of
  models

Model
Testing & Validation

• 
  Define and automate
  testing frameworks for ML
  systems
  , including:
• 
  Unit testing
• 
  Integration testing
• 
  Implement
  validation gates
  and
  model
  promotion criteria
  before deployment to production.

Security
& Compliance

• 
  Collaborate with
  security and compliance teams
  to implement:
• 
  Access controls
• 
  Secrets management
• 
  Audit logging
• 
  Risk management controls

Performance
Optimization

• 
  Optimize infrastructure for
  training and
  inference workloads
  , including:
• 
  Autoscaling
• 
  Resource right-sizing
• 
  GPU utilization
• 
  Workload scheduling
• 
  Ensure efficient
  compute utilization and cost
  optimization
  .

Operational
Excellence

• 
  Develop and maintain:
• 
  Operational runbooks
• 
  SLAs (Service Level Agreements)
• 
  SLOs (Service Level Objectives)
• 
  Incident response processes
• 
  Operational monitoring
  dashboards

Architecture
& Platform Standards

• 
  Contribute to
  reference architectures
  for
  machine learning platforms.
• 
  Develop
  engineering standards, reusable
  templates, and best practices
  for ML product teams.

Required
Skills & Expertise

• 
  Strong experience in
  Machine Learning Operations
  (MLOps)
  and
  ML platform engineering
• 
  Expertise in
  CI/CD pipelines for ML workflows
• 
  Experience managing
  ML model deployment patterns
  (batch, real-time, streaming)
• 
  Knowledge of
  model observability and monitoring
• 
  Hands-on experience with
  feature pipelines and
  feature stores
• 
  Experience implementing
  experiment tracking,
  model registry, and artifact management
• 
  Familiarity with
  model testing frameworks (unit
  and integration testing)
• 
  Strong understanding of
  ML governance, security,
  and compliance practices
• 
  Experience with
  autoscaling infrastructure, GPU
  utilization, and workload scheduling
• 
  Ability to build
  operational dashboards and
  incident management processes
• 
  Strong experience designing
  ML reference
  architectures and reusable engineering templates

Key Focus
Areas

• 
  ML CI/CD pipelines
• 
  Model deployment and serving infrastructure
• 
  Model monitoring and observability
• 
  Feature store management
• 
  Experiment tracking and artifact management
• 
  Testing automation for ML systems
• 
  Security, compliance, and governance
• 
  Cost optimization and GPU utilization
• 
  Operational reliability (SLA/SLO/Incident
  management)

Benefits