Senior Machine Learning Developer (MLOps)

Position Responsibilities:

• Design and Build highly reliable and scalable ML and AI solutions that align with the organizations business goals and objectives
• Lead the creation and implementation of scalable, robust, and high-performance ML architectures including MLOps, AIOps leveraging cloud native services (AWS, Azure, GCP) and open-source frameworks
• Design, build, and optimize machine learning models, ensuring accuracy, efficiency, and scalability
• Collaborate with data engineers, data scientists, software developers, and DevOps teams to integrate ML models into production systems
• Assess and recommend ML tools, frameworks, and platforms to deliver business value and foster innovation
• Design and development of Generative AI and AI use cases (LLMs, RAG, Agentic, multi model AI, fine tuning. Vector databases and prompt engineering)
• Build and optimize continuous integration and continuous deployment (CI/CD) pipelines to enable rapid and reliable software releases
• Automate repetitive tasks, including provisioning, configuration, and monitoring, using tools like Terraform, Ansible, or similar
• Work closely with developers to integrate DevOps and MLOps practices into the software development lifecycle and support containerized applications (e.g., Docker, Kubernetes)
• Own all communication and collaboration channels pertaining to execution of assigned projects, including regular stakeholders, senior leadership and cross-team updates
• Establish working relationships with vendors (Technology and Consulting), partners and cross teams and holding them accountable

This position is hybrid. The selected candidate will be required to perform some work onsite at one of the listed location options. This is at the hiring teams discretion and could potentially change in the future.

Basic Qualifications (Required Skills/Experience):

• A Bachelors degree or higher is required as a BASIC QUALIFICATION
• 5+ years of experience in designing and deploying ML workloads in AWS, Azure or GCP leveraging Kubernetes, Docker and Terraform for containerized and Infrastructure-as-Code environments
• 3+ years of experience with hands-on experience with MLFlow, Kubeflow, SageMaker, Vertext AI or Databricks ML Runtime for model tracking, deployment and CI/CD integration
• 5+ years of progressive experience in DevOps and cloud platform operations.
• 3+ years of hands-on experience designing, implementing, and managing multi-cloud CI/CD pipelines (AWS, Azure, GCP, Databricks, Snowflake).
• 3+ years of experience in Infrastructure as Code (IaC) using Terraform, CloudFormation, or Ansible and experience in containerization using Docker & Kubernetes.
• 3+ years of experience implementing observability, monitoring, and tracing frameworks (Prometheus, Grafana, ELK/EFK, Datadog etc.)
• 3+ years of experience applying FinOps and cost optimization strategies in CI/CD pipelines and cloud operations to drive efficiency, resource accountability, and transparency spend.
• 3+ years of experience in implementing security controls in CI/CD pipelines and cloud platforms (IAM, RBAC, secrets management, encryption).

Preferred Qualifications (Desired Skills/Experience):

• 5+ years of strong scripting and automation skills (Bash, Python, PowerShell, or similar).
• 3+ years of experience in Manufacturing and Aviation
• 3+ years of experience implementing event-driven, cloud-agnostic DevOps architectures supporting microservices, APIs, and serverless workloads.
• 3+ years of experience integrating DevOps, MLOps and expertise in AI/ML-driven observability or anomaly detection into infrastructure and DevOps pipelines.

Typical Education & Experience:

• Education/experience typically acquired through advanced education (e.g. Bachelor) and typically 16 Plus years'' related work experience or Masters Degree with 15+ years of experience with an equivalent combination of education and experience.