Join Zensar's AI Platform Development team to design, build, and deploy custom AI platforms for internal use and clients, working both in-house and on client sites.
Responsibilities
Build AI platforms end-to-end, including services, APIs, web interfaces, data, and tests
Develop AI agents and associated controls such as prompts, tools, retrieval, evaluations, human approval, and audit trails
Propose new features and take them from idea to release
Write clear specifications and review outputs from AI coding assistants
Deploy platforms into client clouds or data centers and integrate with client systems and APIs
Requirements
BS or MS in Computer Science, Computer Engineering, or related field from a strong US university
Design and build custom AI platforms for internal use and clients, deploying them on major clouds or client environments. Engineers build features end-to-end, AI agents, and controls around them.
Locations (2)
San Jose, California, United States
Ann Arbor, Michigan, United States
Responsibilities
Build features end to end: services and APIs, web interfaces, data and tests.
Build AI agents and the controls around them: prompts, tools, retrieval, evaluations, human approval and audit trails.
Understand the roadmap, propose new features, and take them from idea to release.
Work with AI coding assistants every day: write clear specifications, review what they produce, own the result.
Deploy platforms into a client's cloud or data centre and integrate them with the client's systems and APIs.
Requirements
BS or MS in Computer Science, Computer Engineering or a related field from a strong US university
As an early team member, you will get a chance to shape both the foundations of the product, backend architecture, and team culture. Our client will measure your success by your ability to ship fast, learn quickly, and contribute to our culture of continuous improvement.
Compensation
Competitive equity as a founding employee of a startup funded by top tier investors
Responsibilities
Design and build server-side Human L1 and blockchain architecture (using Golang, Cosmos SDK, AWS, Azure)
Write technical documentation
Manage the server-side technical team
Develop and manage well-functioning decentralized databases
Design and build back-end services that support data-centric clinical and analytic applications leveraging cloud computing, big data, mobile, data science, data warehousing, machine learning.
Locations (4)
Rochester, Minnesota, United States
Phoenix, Arizona, United States
Scottsdale, Arizona, United States
Jacksonville, Florida, United States
Responsibilities
Design and build back-end services for clinical and analytic applications
Ensure cloud-based micro-services adhere to uptime and accuracy targets, are resilient, and scale
Develop applications around data warehousing, big data, cloud computing, business intelligence, analytics and machine learning
Own the end-to-end ML lifecycle, from dataset creation and foundational research to building and deploying production-grade models.
Responsibilities
Research, design, and implement novel machine learning solutions using modern architectures to tackle complex business problems.
Build and manage efficient pipelines for rapid experimentation and hypothesis testing.
Methodically design, execute, and track all experiments, including hyperparameter searches, architecture changes, and data variations, using tools like MLflow or Weights & Biases.
Deploy models into production environments using CI/CD practices and model serving frameworks.
Implement and maintain robust monitoring systems to track model performance, detect drift, and ensure reliability and scalability.
Requirements
Bachelor's or Master's degree in Computer Science, AI, Data Science, or a related technical field
Design, develop, and optimize machine learning and generative AI systems for pharmaceutical marketing content evaluation.
Compensation
Salary: $140,000 to $250,000 per year.
Responsibilities
Design, develop, and optimize machine learning and generative AI systems used to evaluate pharmaceutical marketing and promotional content for scientific accuracy, evidentiary support, and regulatory compliance.
Develop large language model applications using prompt engineering, retrieval-augmented generation (RAG), multi-agent orchestration, model experimentation, fine-tuning, and performance benchmarking to analyze clinical literature, prescribing information, regulatory guidance, and other life sciences source materials.
Build machine learning pipelines for content classification, claim extraction, source attribution, evidence verification, and contextual compliance analysis.
Design and deploy scalable production infrastructure supporting AI applications, including model serving, retrieval systems, data processing, evaluation frameworks, testing, monitoring, and system integrations.
Develop and maintain evaluation systems to measure model factuality, groundedness, accuracy, reliability, and compliance-related performance.
Requirements
Master's degree, or foreign equivalent, in Computer Science or a related field
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