Ai Platform Engineer

We are looking for an AI Platform Engineer to lead the adoption, integration, and operationalization of AI tooling across our organization. This role will focus on enabling teams to work more efficiently by implementing AI-powered solutions, best practices, and MLOps strategies. The ideal candidate has proven experience working with engineering and technology companies to drive AI adoption across engineering, sales, and marketing , ensuring AI initiatives deliver measurable impact and efficiency gains.

This role requires a combination of technical expertise in AI / ML infrastructure, strategic thinking to identify high-value AI opportunities, and cross-functional collaboration to help teams seamlessly integrate AI into their workflows. The AI Platform Engineer will play a key role in automating processes, optimizing team operations, and ensuring AI solutions align with business goals .

Key Responsibilities :

• AI Tooling & Infrastructure : Design, implement, and scale AI / ML tooling that improves team efficiency, streamlines workflows, and enhances automation.
• MLOps Best Practices : Help engineering teams implement effective MLOps processes, including model deployment, monitoring, and lifecycle management.
• Cross-Team Collaboration : Work closely with software engineering, data science, and operations teams to integrate AI capabilities into their workflows.
• AI Adoption & Enablement : Drive organization-wide adoption of AI by identifying key pain points, providing education, and ensuring seamless integration of AI solutions.
• Automation & Efficiency : Develop AI-powered automation solutions to reduce manual work, improve productivity, and optimize team operations.
• Scalability & Performance : Ensure AI infrastructure and workflows are scalable, resilient, and efficient to support company-wide initiatives.
• Security & Compliance : Implement AI governance, security best practices, and compliance measures to align with organizational and regulatory standards.
• Monitoring & Evaluation : Establish metrics and feedback loops to measure the impact of AI tooling on team efficiencies and continuously improve processes.

Qualifications :

Technical Expertise :

• 5+ years of experience in software engineering, MLOps, or AI infrastructure roles.
• Strong programming skills in Python, Go, or another relevant language.
• Experience with AI / ML frameworks such as TensorFlow, PyTorch, or Hugging Face.
• Proficiency in cloud platforms (AWS, GCP, or Azure) and AI / ML services.
• Hands-on experience with containerization (Docker, Kubernetes) and orchestration tools.
• Familiarity with AI observability, monitoring, and governance tools.
• Strong understanding of CI / CD pipelines, infrastructure as code (Terraform, Pulumi), and automation best practices.

MLOps & AI Operations :

• Experience implementing MLOps pipelines for model training, deployment, and monitoring.
• Familiarity with vector databases, feature stores, and AI-powered search.
• Knowledge of distributed computing and optimizing AI workloads for efficiency.
• Soft Skills & Adoption Focus :
• Excellent problem-solving and analytical skills to identify high-impact AI solutions.
• Ability to work cross-functionally and communicate effectively with non-technical stakeholders.
• Strong ability to drive change, facilitate AI adoption, and enable teams to succeed with AI-driven processes.
• Passion for AI automation, operational efficiency, and developer experience.

Success Metrics for This Role :

• Adoption Rate : Increase in AI tooling adoption across engineering, sales, and marketing teams.
• Efficiency Gains : Measurable reductions in manual effort and time spent on repetitive tasks.
• MLOps Maturity : Improved model deployment velocity and reliability through MLOps best practices.
• Developer Productivity : Reduction in friction for AI-enabled engineering teams.
• Feedback & Iteration : Continuous improvement based on team feedback and usage metrics.

Please answer the questions in detail in the given link to move forward with the screening process.

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