Uniphore is one of the largest B2B AI-native companies—decades-proven, built-for-scale and designed for the enterprise. The company drives business outcomes, across multiple industry verticals, and enables the largest global deployments.
Uniphore infuses AI into every part of the enterprise that impacts the customer. We deliver the only multimodal architecture centered on customers that combines Generative AI, Knowledge AI, Emotion AI, workflow automation and a co-pilot to guide you. We understand better than anyone how to capture voice, video and text and how to analyze all types of data.
As AI becomes more powerful, every part of the enterprise that impacts the customer will be disrupted. We believe the future will run on the connective tissue between people, machines and data: all in the service of creating the most human processes and experiences for customers and employees.
Job Description:
Job Overview
As a Senior Software Engineer at Uniphore, you’ll help build and evolve the data platform and AI capabilities at the heart of our product offerings. You’ll work closely with engineers, product managers, and AI/ML teams to deliver scalable, reliable, high-performance systems in the cloud—especially for unstructured data, RAG, and agentic AI workflows.
Key Responsibilities
Build and enhance a scalable data engineering platform across cloud providers (AWS/GCP/Azure).
Design and implement distributed data applications using Spark, Databricks, and/or Snowflake.
Develop and ship AI-driven applications, including RAG pipelines and agentic workflows (tool orchestration, multi-step execution).
Implement robust processing for unstructured data (documents, PDFs, transcripts, chat logs), including extraction, enrichment, and indexing for downstream analytics/AI.
Write clean, maintainable, and efficient code aligned with engineering best practices.
Participate in the full SDLC: requirements, design, development, testing, and release.
Troubleshoot, debug, and optimize existing services for performance, reliability, and scalability.
Collaborate with cross-functional teams (AI/ML, product, UX) to translate business needs into technical solutions.
Ensure adherence to security and data privacy standards when working with sensitive customer data.
Contribute to and improve CI/CD pipelines, deployment automation, and code quality processes.
Required Skills and Experience
Bachelor’s/Master’s degree in Computer Science, IT, or equivalent practical experience.
3–5 years of professional software development experience.
Strong proficiency in Java and Python, with experience building APIs/services.
Experience with frameworks such as Spring Boot or Vert.x.
Working knowledge of databases such as Postgres, MongoDB, or MySQL.
Experience with one or more cloud platforms: AWS, GCP, or Azure.
Strong problem-solving and debugging skills; ability to work independently and drive deliverables.
Familiarity with software engineering best practices: version control, code reviews, testing/TDD.
Strong written and verbal communication skills; comfortable in a fast-paced environment.
Preferred Skills
Hands-on experience with Spark or managed Spark (e.g., Dataproc, Databricks).
Familiarity with orchestration tools such as Airflow.
Experience with cloud data warehouses like Snowflake or BigQuery.
Experience building/operating RAG systems (chunking, embeddings, vector search, evaluation, guardrails).
Familiarity with agentic/agent frameworks for tool use, orchestration, and multi-step workflows.
Experience with unstructured data processing pipelines and search/indexing patterns.
Familiarity with containers and Kubernetes.
Exposure to DevOps tools like Jenkins and CI/CD workflows.
Basic Linux fluency.
Familiarity with JavaScript/TypeScript.
Prior exposure to AI/ML implementation projects.
Location preference:
Uniphore is an equal opportunity employer committed to diversity in the workplace. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, disability, veteran status, and other protected characteristics.
For more information on how Uniphore uses AI to unify—and humanize—every enterprise experience, please visit www.uniphore.com.
Top Skills
What We Do
The Uniphore Business AI Cloud spans agents, models, knowledge, and data, combining the simplicity of consumer AI with the rigor, security, and scalability the enterprise demands. Business users can harness AI effortlessly and deliver outcomes immediately, while CIOs gain the foundation to build powerful AI applications embedded into workflows and trained on enterprise data. Built for the Enterprise, backed by AI Leaders Backed by NVIDIA, AMD, Snowflake, and Databricks, Uniphore brings a unique combination of capital and strategic alignment — validating its position as the definitive Business AI leader. Recent acquisitions of ActionIQ, Infoworks, Orby AI, and Autonom8 further cement Uniphore's AI talent depth and accelerate outcomes for clients. A Complete, Composable Platform Uniphore is designed to be: Sovereign — run on any public cloud, private cloud or on-premises with full control over your data and AI models. Composable — choose your layer, model, or component—vector DBs, knowledge graphs, data compute, and beyond. Secure — embedded guardrails, observability, and AI security ensure trusted, compliant, and enterprise-grade protection. Trusted at Scale Over 2,000 global businesses — including many of the Fortune 500 — rely on Uniphore every day to drive growth, improve efficiency, and deliver personalized customer experiences. Customers include leaders across industries, like Skechers, LastPass, Atlassian, HP, Allstate, Sony, and more. Industry Recognition Named to Inc.'s Best in Business List Listed on the Deloitte Technology Fast 500 Recognized in reports by Gartner, Forrester, and IDC From Pilot to Production Through strategic collaborations with industry leaders like KPMG, Cognizant, Rackspace, Databricks and Snowflake, Uniphore helps organizations move beyond experimental AI pilots to production-grade deployment — operationalizing AI agents across internal and client-facing workflows at scale.
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