Jobs

About Oversight

Oversight is the world’s leading provider of AI-based spend management and risk mitigation solutions for large enterprises. Based in Atlanta, GA, Oversight works with many of the world’s most innovative companies and government agencies to digitally transform their spend audit and financial control processes.

Oversight’s AI-powered platform works across our customers’ financial systems to continuously monitor and analyze all spend transactions for fraud, waste, and misuse. With a consolidated, consistent view of risk across their enterprise, customers can prevent financial loss and optimize spend while strengthening the controls that improve compliance. Learn More.

Position Overview:

We are seeking a skilled and forward-looking ML Engineer with experience in Large Language Models (LLMs), generative AI, and agentic architectures to join our growing R&D and Applied AI team. This role is critical in helping Oversight deliver the next generation of agentic AI systems for enterprise spend management and risk controls.

The ideal candidate has a strong foundation in machine learning, modern deep learning frameworks, and data pipelines, coupled with hands-on experience experimenting with LLMs, small language models (SLMs), multi-agent frameworks, and retrieval-augmented generation (RAG).

You will work closely with AI/ML researchers, data engineers, and product teams to design, implement, and optimize models that power autonomous exception resolution, anomaly detection, and explainable insights. This is a hands-on engineering role where you will not only build and scale ML systems but also actively contribute to cutting-edge applied research in agentic AI.
Core ML/LLM Engineering
• Contribute to the design, training, fine-tuning, and deployment of ML/LLM models for production.
• Implement RAG pipelines using vector databases.
• Work with frameworks like LangChain, LangGraph, MCP to prototype and optimize multi-agent workflows.
• Develop prompt engineeri

About the role

As a Senior QA Automation Engineer with Playwright with Typescript experience, you will make an impact by ensuring high quality software releases through robust test strategy, hands on automation, and close collaboration with cross functional teams. You will be a valued member of the delivery team and work collaboratively with Product Owners, developers, and business stakeholders to drive quality across the software development lifecycle.

In this role, you will:
• Lead and support release testing activities, ensuring quality and readiness for production deployments
• Collaborate closely with Product Owners, developers, and stakeholders to understand requirements and define test coverage
• Develop, enhance, execute, and debug automated test scripts using Playwright and TypeScript
• Design and execute manual test scenarios and test cases where automation is not applicable
• Define and maintain test strategies, test plans, and testing estimates aligned with Agile delivery practices

Work model
At Cognizant, we strive to provide flexibility wherever possible, and we are here to support a healthy work life balance through our various wellbeing programs. Based on this role’s business requirements, this is an onsite position requiring a minimum of 3 days a week in a Cognizant or client office in Fort Mill, SC.

The working arrangements for this role are accurate as of the date of posting. This may change based on the project you’re engaged in, as well as business and client requirements. Rest assured, we will always be clear about role expectations.

What you need to have to be considered
• 7+ years of experience in Quality Assurance with a strong foundation in Agile practices
• Proven expertise in writing, enhancing, executing, and debugging automated tests using Playwright
• Strong hands on experience with TypeScript
• Exposure to or hands on experience with LLM models and Amazon Bedrock
• Solid experience in manual testing, including test scenario and t

About the Role

We are seeking a Lead ML Engineer, LLM Post-Training Infrastructure to join the Salesforce AI Research Incubation Team. In this role, you will own the infrastructure and engineering systems that support LLM post-training, large-scale evaluation, and model deployment. You will build scalable, reliable

pipelines for training orchestration, rollout generation, reward and feedback pipelines, experiment management, and model iteration, helping translate research ideas into production-grade systems.

This is an engineering-first role focused on ML infrastructure, distributed systems, and training/evaluation workflows rather than developing new model architectures or algorithms. You will work closely with research scientists, agent engineers, and platform teams to operationalize post-training and feedback-driven learning methods into robust, reusable systems.This is a lead-level individual contributor role with deep ownership of model-facing infrastructure and strong cross-functional influence.

Key Responsibilities:

● Design, build, and maintain infrastructure for LLM post-training, evaluation, and

deployment.

● Own scalable pipelines for training orchestration, rollout generation, reward and

feedback processing, checkpointing, and experiment management.

● Build reliable systems for feedback-driven model improvement, including human or AI

feedback loops, large-scale offline evaluation, and regression detection.

● Partner closely with research scientists to turn new post-training methods into reusable

engineering workflows.

● Collaborate with agent engineers and platform teams to integrate training and evaluation

systems with production model and agent stacks.

● Optimize distributed training and inference workloads for reliability, throughput, cost

efficiency, and observability.

● Drive best practices for reproducibility, versioning, monitoring, deployment, and

operational excellence across ML systems.

Required Qualifications:

● 5+ years of