Lead AI Ops Engineer

About Apexon:

Apexon is a digital-first technology services firm specializing in accelerating business transformation and delivering human-centric digital experiences. We have been meeting customers wherever they are in the digital lifecycle and helping them outperform their competition through speed and innovation. Apexon brings together distinct core competencies – in AI, analytics, app development, cloud, commerce, CX, data, DevOps, IoT, mobile, quality engineering and UX, and our deep expertise in BFSI, healthcare, and life sciences – to help businesses capitalize on the unlimited opportunities digital offers. Our reputation is built on a comprehensive suite of engineering services, a dedication to solving clients’ toughest technology problems, and a commitment to continuous improvement. Backed by Goldman Sachs Asset Management and Everstone Capital, Apexon now has a global presence of 15 offices (and 10 delivery centers) across four continents. 

We enable #HumanFirstDIGITAL

Job Summary: The Lead AI Ops role is responsible for leading the design and implementation of productionized Artificial Intelligence (AI) solutions to solve business problems. You will work closely with our data science teams and other stakeholders to enable the integration of AI/ML models into business processes.  These solutions will be scalable, resilient, and secure.

This role will maintain CI/CD pipelines, productionize models (ML/DL/LLM), and develop integration code needed to deploy AI solutions.

Essential Job Functions with %

• AI Solution Development (35%) Lead the design and implementation of productionized models (ML/DL/LLM) solutions that are scalable, resilient, and secure. Evaluate and optimize the data science methodology needs while meeting the non-functional requirements of the business process.
• Research and Development (25%) Keep up to date with the latest technology trends. Research new ways for applying AI solutions in our business.
• Model Monitoring and Maintenance (20%) Monitor and maintain the performance of deployed models. Monitor production performance and provide recommendations for maximizing ML/LLM configurations and performance.  Assist tool/platform admins with health and maintenance of the ecosystem.
• Deployment Pipeline Management (10%): Develop and maintain release pipelines for data science teams that support Continuous Integration (CI), Continuous Deployment (CD). Automate build and deployment procedures to streamline delivery. Schedule and validate all production deployments. Partner with Release Management, Infrastructure, DevOps, etc. to ensure a smooth and successful deployment.
• Team Leadership and Collaboration (10%) Lead and mentor team of AI Ops developers. Promote AI/ML Ops practices within the Data Science community. Collaborate with different teams to implement models and monitor outcomes. Drive cross-functional projects. Provide updates to stakeholders on status of deployments and any risks & issues. 

• Problem Solving
• Agile / Product oriented development
• Data science / statistical modeling techniques
• AI Ops / ML Ops / ML Engineering
• Designing scalable Model as a Service solutions
• Research into new technologies, tools, techniques
• Can work on multiple concurrent initiatives
• Team leadership, mentorship, coaching

• Python, SQL, R, Scala
• Predictive modeling, machine learning, data engineering, data operations
• ML engineering frameworks, including ML Flow, Airflow, Langchain, Langfuse, LLM Guard, etc.
• Cloud based tools, including Kubernetes, Docker, AWS, Azure, APIMs
• Understanding of Machine Learning techniques and algorithms
• Deploying API endpoints
• Troubleshooting and diagnosing production deployments of AI solutions

• Bachelor’s degree in Statistics, Mathematics, Engineering, Data Science, Computer Science.
• 10+ years of professional experience in   AI Ops, ML engineering, data sc