Senior AI Engineer

We are

At Synechron, we believe in the power of digital to transform businesses for the better. Our global consulting firm combines creativity and innovative technology to deliver industry-leading digital solutions. Synechron’s progressive technologies and optimization strategies span end-to-end Artificial Intelligence, Consulting, Digital, Cloud & DevOps, Data, and Software Engineering, servicing an array of noteworthy financial services and technology firms. Through research and development initiatives in our FinLabs we develop solutions for modernization, from Artificial Intelligence and Blockchain to Data Science models, Digital Underwriting, mobile-first applications and more. Over the last 20+ years, our company has been honored with multiple employer awards, recognizing our commitment to our talented teams. With top clients to boast about, Synechron has a global workforce of 17000, and has 58 offices in 22 countries within key global markets.

About the Role

We are seeking a hands-on Senior AI Engineer to design, build, and deploy production-grade artificial intelligence solutions. The ideal candidate combines strong software engineering fundamentals with practical experience developing, integrating, and operating AI systems at scale.

This role requires deep proficiency in Java and Python , experience with Google Cloud Platform (GCP) , and a proven track record of taking AI and machine learning capabilities from concept through production. You will work closely with product managers, data scientists, software engineers, and business stakeholders to deliver reliable, scalable, and measurable AI solutions.

Additional Information*

The base salary for this position will vary based on geography and other factors. In accordance with law, the base salary for this role if filled within New York, NY is $115k - $130k/year & benefits (see below).

Key Responsibilities

• Design, develop, and maintain production-grade AI and machine learning applications.
• Build scalable backend services, APIs, data pipelines, and AI platforms using Java and Python .
• Develop and integrate solutions involving generative AI, machine learning models, natural language processing, recommendation systems, or other applied AI technologies.
• Deploy, monitor, optimize, and troubleshoot AI workloads on GCP .
• Work with GCP services such as Vertex AI, BigQuery, Cloud Run, GKE, Pub/Sub, Cloud Storage, Cloud Functions, and related platform technologies.
• Develop reliable model-serving and inference systems with a focus on performance, scalability, security, and cost efficiency.
• Implement production AI practices, including model versioning, evaluation, observability, drift monitoring, testing, and continuous improvement.
• Collaborate with data scientists and ML engineers to productionize models and integrate them into enterprise applications.
• Design and implement secure, maintainable, and well-tested software using modern engineering practices.
• Participate in architecture reviews, technical design discussions, code reviews, and engineering standards initiatives.
• Investigate emerging AI technologies and assess their applicability to business and product needs.
• Mentor engineers and contribute to the development of best practices for AI engineering and cloud-native software delivery.

Required Qualifications

• 7–10 years of professional software engineering experience , including substantial experience delivering AI or machine learning solutions.
• Strong hands-on programming experience with Java and Python .
• Proven experience building and operating production AI/ML systems , not only research or proof-of-concept applications.
• Strong experience with Google Cloud Platform , including cloud-native application development, deployment, monitoring, and security.
• Experience designing and developing RESTful APIs, microservices, distributed systems, and event-driven architectures.
• Solid understanding of machine learning concepts, model lifecycle management, data preparation, evaluation, and inference.
• Experience with software engineering best practices, including: Automated testing, CI/CD ,Version control, Code quality and review, Containerization, Monitoring and observability
• Experience deploying applications with Docker and Kubernetes or GKE.
• Strong understanding of application security, scalability, reliability, and performance optimization. -…