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.
Role Description:
Job Summary
The Practice Lead AI & ML Engineer is responsible for leading the design, development, and deployment of production-grade Artificial Intelligence (AI) and Machine Learning (ML) solutions to solve complex business challenges. The role requires working closely with data science teams and business stakeholders to integrate AI/ML models into scalable, resilient, and secure business processes. The individual will maintain CI/CD pipelines, produce ML/DL/LLM models, and develop robust integration code to enable seamless deployment of AI solutions.
Essential Job Functions
1. AI Solution Development
o Lead the design and implementation of productionized models (ML/DL/LLM) that meet scalability, security, and resilience requirements.
o Evaluate data science methodologies and ensure their alignment with business processes.
o Optimize AI/ML solutions for performance, reliability, and cost-efficiency.
2. Research and Development
o Stay up to date with emerging technologies, tools, and trends in AI and ML fields.
o Research innovative methods to integrate AI solutions into business processes.
o Drive the adoption of new tools, platforms, and engineering practices within the team.
3. Model Monitoring and Maintenance
o Monitor the performance of deployed AI/ML models and make recommendations for optimization.
o Troubleshoot production issues and proactively address risks in model configurations.
o Assist platform administrators in maintaining the health of the AI/ML ecosystem.
o Develop and maintain CI/CD pipelines to support Continuous Integration and Deployment processes for AI/ML models.
o Automate build, deployment, and validation procedures to streamline AI solution delivery.
o Collaborate with Infrastructure, Release Management, and DevOps teams to ensure smooth production deployments.
4. Team Leadership and Collaboration
o Lead and mentor a team of AI/ML Engineers, promoting best practices in AI/ML Ops.
o Collaborate with cross-functional teams, including Data Science, Product, and DevOps, to ensure successful solution delivery.
o Provide regular updates to stakeholders on deployment status, risks, and outcomes.
Required Skills and Competencies
* Strong problem-solving capabilities with the ability to work on multiple concurrent initiatives.
* Agile and product-oriented development expertise.
* Expertise in AI/ML Ops, ML Engineering, and data science methodologies.
* Strong understanding of designing scalable Model-as-a-Service solutions.
* Ability to research and adopt emerging technologies and tools for AI/ML solutions.
* Proven leadership skills to mentor and guide a team, ensuring quality outcomes.
Technical Skills:
* Programming: Python, SQL, R, Scala
* Tools & Frameworks: ML Engineering tools such as MLflow, Airflow, Langchain, Langfuse, LLM Guard.
* Machine Learning: Experience in predictive modeling, machine learning algorithms, and statistical techniques.
* Cloud Platforms: Proficiency in Azure, AWS, and containerization technologies like Docker and Kubernetes.
* Integration: Expertise in APIs, deploying endpoints, and troubleshooting production deployments.
* Deployment Pipelines: Hands-on experience in CI/CD pipeline development and production deployments.
Knowledge Areas:
* Understanding of ML/LLM techniques, algorithms, and data operations.
* Experience in deploying and monitoring scalable AI/ML solutions.
* Strong analytical and troubleshooting skills to diagnose deployment and performance issues.
Preferred Qualifications
* Bachelor's degree in Statistics, Mathematics, Engineering, Data Science, or Computer Science.
* 10+ years of experience in AI Ops, ML Engineering, Data Science, DevOps, Data Engineering, or related fields.
* Experience working in multidisciplinary teams on large-scale AI/ML projects.
Don’t worry if you don’t check all the boxes; we’d still love to hear from you.
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