Ml Engineer Jobs

About The Position

Requirements

• Extensive practical experience with demonstrated success developing effective business solutions or applications for products or services that may affect broad areas of the organization.
• Recognized expertise and industry knowledge as an individual contributor to operations.
• Ability to set objectives and deliver results affecting a department or division.
• Ability to provide advice, counsel, and thought leadership within a department.
• High-level strategic influence, with decisions that affect a business unit's or department's strategic direction.
• Ability to anticipate emerging trends and take a forward-looking, multi-year perspective.
• High degree of autonomy and independent discretion when handling complex, strategic duties requiring functional expertise.
• Ability to navigate ambiguous organizational challenges and develop innovations or adaptations.

Nice To Haves

• Bachelor's or master's degree in Computer Science, Machine Learning, Data Science, or a related field.
• Strong programming skills in Python, Java, or Scala.
• Expertise with ML frameworks such as TensorFlow, PyTorch, or scikit-learn and orchestration tools such as Airflow, Kubeflow, Vertex AI, or MLflow.
• Experience operating production pipelines for ML and LLM-based systems across GCP, AWS, or Azure.
• Familiarity with LangChain, LangGraph, ADK, or similar agentic system runtime management.
• Strong competencies in CI/CD, Infrastructure as Code, and DevSecOps pipelines integrating testing, compliance, and deployment automation.
• Hands-on experience with observability stacks such as Prometheus, Grafana, or New Relic for model and agent performance tracking.
• Understanding of Responsible AI governance, auditability, and cost metering for training and inference workloads.
• Proficiency with containerization technologies such as Docker and Kubernetes.
• Proficiency in scripting languages such as Bash or Python for automation.
• Experience with workflow orchestration tools such as Apache Airflow.
• Expertise managing and optimizing cloud-based infrastructure.
• Familiarity with DevOps practices and tools for automated deployment.
• Understanding of network configurations and security protocols.
• Ability to define problems, collect and analyze data, and propose innovative solutions.
• Strong critical-thinking skills for evaluating models and identifying limitations.
• Adaptability in a fast-paced, rapidly evolving environment and proactive learning about trends, techniques, and technologies in AI and data science.

Responsibilities

• Partner with data science, security, and product teams to establish evaluation and governance standards for guardrails, bias, drift, and latency SLAs.
• Mentor senior engineers and lead design reviews for ML pipelines, model registries, and agentic runtime environments.
• Lead incident response and reliability strategies for ML and AI systems.
• Collaborate with development teams to integrate AI solutions into existing workflows, applications, platforms, and technologies.
• Define and manage an MCP Registry for agentic component onboarding, lifecycle versioning, and dependency governance.
• Build CI/CD pipelines for LLM agent deployment, policy validation, and prompt evaluation.
• Develop experimentation frameworks for agent evaluations, scenario regression, and performance analytics.
• Implement logging, metering, and auditing for agent behavior and function calls, and support compliance alignment.
• Create observability systems tracking conversation outcomes, factual accuracy, latency, escalation patterns, and safety events.
• Architect AI-agent guardrails, including prompt-injection protection, identity-aware routing, and tool-use authorization.
• Collaborate across teams to standardize authentication, authorization, and session governance for multi-agent runtimes.
• Architect and standardize model registries and feature stores for version tracking, lineage, and reproducibility.
• Lead deployment of machine learning models into production environments, ensuring scalability, reliability, and efficiency.
• Collaborate with software engineers to integrate models into applications and systems, and implement and maintain model-inference APIs.
• Design and manage training infrastructure, including distributed training orchestration, GPU/TPU allocation, and automatic scaling.
• Implement CI/CD for model workflows, including model validation, bias checks, and rollback automation.
• Build reproducible experimentation frameworks for training, tuning, and deployment cycles.
• Manage and optimize cloud infrastructure required for machine learning operations, supporting system availability, security, and performance.
• Monitor deployed models and work with data scientists and engineers to resolve model-performance and data-quality issues.
• Perform maintenance, updates, and optimizations for machine learning solutions.
• Develop automation scripts and tools and implement orchestration systems for the ML lifecycle, from data preparation through model deployment.
• Work with data scientists to understand deployment requirements and constraints and transition models from research to production.
• Analyze production performance and resource utilization and implement improvements to model efficiency.
• Implement security measures and support regulatory and industry-standard compliance for ML systems and data privacy.
• Integrate audit controls, metadata storage, and lineage tracking across ML and AI workflows.
• Establish monitoring and feedback loops, including event logs, evaluations, and automated retraining triggers.
• Enforce secure deployment patterns using Infrastructure as Code and cloud-native secrets management.
• Define SLAs, error budgets, and compliance reporting mechanisms for ML and AI systems.

Benefits

• Professional development seminars.
• Inclusive affinity groups.
• Short-term and long-term incentive programs, based on eligibility.
• Benefit and wellness plans.