Senior AI/ML Engineer at nimble International

Hyderabad, Telangana, India -

Full Time

Start Date

Immediate

Expiry Date

14 Jan, 26

Salary

0.0

Posted On

16 Oct, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Python, SQL, MLOps, NLP, Document Processing, Data Engineering, Collaboration, Mentoring, Automation, Feature Engineering, Cloud Computing, Quality Assurance, Dashboarding, Communication, Healthcare

Industry

Hospitals and Health Care

Description

Description Why work at nimble? This is a great opportunity to join a well-established and market-leading brand serving a high-growth end market while gaining valuable experience working closely with Executive leadership. As an organization, we are in high-growth mode through acquisition with a laser focus on positive culture building! Who we are! nimble solutions is a leading provider of revenue cycle management solutions for ambulatory surgery centers (ASCs), surgical clinics, surgical hospitals, and anesthesia groups. Our tech-enabled solutions allow surgical organizations to streamline their revenue cycle processes, reduce administrative burden, and improve financial outcomes. Join over 1,100 surgical organizations that trust nimble solutions and its advisors to bring deep insights and actionable intelligence to maximize their revenue cycle. What you'll do! Design, build, and run machine learning systems that make healthcare revenue operations faster, more accurate, and less manual. You will work closely with our existing AI team and partner functions to turn data into reliable automation and decision support. Why this role matters Your work will reduce preventable denials, shorten turnaround times, improve first pass yield, and lower the cost to collect. You will help set the standards for how we use AI across the company. What you will do Turn business problems into ML solutions with clear goals and success metrics Build, deploy, and operate ML services used by internal teams and clients Set up automated tests and dashboards that track quality, speed, cost, and drift, with clear rollback criteria Work with data engineering to prepare clean, well-documented datasets from claims, remits, eligibility, status, and operational logs Collaborate daily with the existing AI team, product managers, and platform engineers to land models in real workflows with privacy and audit controls Write clear documentation, playbooks, and handoffs so others can run and extend your work Mentor engineers and analysts, and contribute to hiring as the practice grows Build and refine NLP pipelines for document analysis, OCR, and entity extraction Work closely with cross-functional teams (Product, Frontend, QA) to align AI/ML solutions with business goals Mentor junior engineers in best practices for model design, coding, and MLOps Requirements What you will bring 5 plus years of ML engineering with models serving real users Strong Python and SQL and hands-on experience with a major ML framework Practical MLOps skills such as pipelines, versioning, monitoring, alerts, and safe releases in a public cloud Experience turning messy operational data into features. Healthcare or RCM exposure is a plus Clear communication and a habit of shipping and measuring impact Nice to have NLP on clinical or operational text, or experience with speech post-processing Retrieval augmented systems that combine search with ML or LLMs Familiarity with modern lakehouse and BI semantic layers and working with regulated data Experience with OCR libraries (Tesseract, AWS Textract) or advanced document processing What success looks like In 90 days, you will have a production ML service with automated tests and live dashboards, and teams will be using it In 6 months, your work improves a core KPI such as denial rate, turnaround time, or first pass yield, and the pattern is reused in another area Ongoing, you help set team standards and strengthen day-to-day collaboration with the existing AI group

Responsibilities

Design, build, and run machine learning systems to enhance healthcare revenue operations. Collaborate with teams to turn data into reliable automation and decision support.