Staff Machine Learning Engineer (Employer of Record)

Credit Acceptance Corporation; India; Hybrid; INR 6,355,839 - 9,321,897; Full time

Job summary

Credit Acceptance Corporation in India is seeking a Staff Machine Learning Engineer (MLE) to partner with business and engineering teams. You will lead the development of AI-powered solutions, leveraging advanced machine learning techniques and frameworks.

The ideal candidate has at least a PhD in a relevant field with extensive experience in deploying AI models and will be responsible for architecting enterprise-grade LLM-powered solutions while working with a diverse team. A compensation package between ₹63,55,839 and ₹93,21,897 is offered.

Qualifications

• 5+ years of experience in machine learning and software engineering.
• Hands-on experience with LLM models.
• Strong problem-solving skills and collaborative mindset.

Responsibilities

• Explore advanced machine learning techniques to solve complex challenges.
• Collaborate with stakeholders to define strategic roadmaps.
• Design and deliver scalable AI systems.

Skills

Machine Learning

Deep Learning

Statistical Analysis

AI model deployment

Data Science

Education

PhD in Computer Science or related field

Master's Degree in relevant technical field

Tools

Databricks

Apache Airflow

MLflow

Kubeflow

Job description

Company Overview

Credit Acceptance is an award‑winning company with workplace recognition in multiple categories. It is one of the largest used car finance companies in the United States.

Role Overview

As a Staff MLE within the AI team, you will partner with business and engineering stakeholders to formulate the vision to achieve the company’s strategic goals. You will co‑lead the roadmap to deliver innovative solutions for dealers, consumers and team members, and lead the development of AI‑powered solutions across different business areas.

Responsibilities

ML Outcomes:

• Explore and apply advanced machine learning techniques, including large language models (LLMs), deep learning, and graph neural networks, to solve complex challenges across the organization.
• Collaborate with management and stakeholders to define strategic roadmaps and translate them into actionable quarterly plans.
• Drive execution and delivery of ML/AI solutions by managing priorities, deadlines, and deliverables, leveraging technical expertise.
• Design and deliver scalable, secure systems using state‑of‑the‑art AI/ML technologies and industry best practices.
• Troubleshoot and resolve complex technical issues to improve system reliability, scalability, and operational efficiency.
• Ensure the security, scalability, and architectural integrity of feature designs through reviews across teams.
• Deliver hands‑on solutions while mentoring other data professionals (including MLEs) within the organization.

Gen‑AI Outcomes:

• Architect and implement enterprise‑grade LLM‑powered solutions, managing the full lifecycle from business requirements to production deployment, monitoring, and continuous optimization.
• Design and develop multi‑agent GenAI systems using state‑of‑the‑art frameworks (LangChain, LlamaIndex) to orchestrate complex workflows across retrieval augmentation, data operations, and compliance verification.
• Engineer robust Retrieval Augmented Generation (RAG) pipelines incorporating advanced techniques such as hybrid retrieval, reranking, query expansion, and contextual compression.
• Implement parameter‑efficient fine‑tuning strategies (LoRA, QLoRA, PEFT) to adapt foundation models to domain‑specific use cases while optimizing for inference costs and latency.
• Develop intelligent routing and orchestration systems to manage conversation state across multiple specialized AI agents.
• Build evaluation frameworks to measure and improve LLM performance across diverse metrics, including factuality, coherence, task completion, and alignment with business objectives.
• Integrate LLM solutions with existing enterprise architecture, ensuring compliance with data security policies, authentication mechanisms, and transaction safety requirements.

Competencies

• Customer Empathy: Understand the perspectives, pain points, and experiences of customers to provide a better customer‑centric experience.
• Engineering Excellence: Bring great craftsmanship and thought leadership to deliver an outstanding product that delights customers and solves for the business.
• One Team: Collaborate seamlessly across the organization, working together without boundaries.
• Owner’s Mindset: Act with responsibility, accountability, and proactive management of your domain.

Required Qualifications

• PhD in Computer Science, Statistics, Economics, or a relevant technical field with at least 5+ years of relevant experience, or MS with at least 8+ years of experience in machine learning and software engineering.
• 6+ years of hands‑on experience designing, building and deploying AI (ML, DL, Gen‑AI) models, including reinforcement learning algorithms, recommendation systems, transformers, fine‑tuned LLMs, causal inference, regressions, etc.
• Strong problem‑solving skills with a bias for action.

ML Skills

• Experience designing and deploying pipelines using DAGs (e.g., Kubeflow, DVC, Ray).
• Ability to construct batch and streaming microservices exposed as gRPC and/or GraphQL endpoints.
• Experience with Databricks MLflow for ML lifecycle management and model versioning.
• Hands‑on experience with Databricks Model Serving for production ML deployments.
• Expertise in data science, advanced experimentation and visualization techniques.
• Experience in model interpretability and responsible AI practices.
• Experience in the automotive industry, especially in building ML/AI systems while ensuring local and central regulations.

GenAI Skills

• Demonstrable experience in parameter‑efficient fine‑tuning, model quantization, and quantization‑aware fine‑tuning of LLM models.
• Hands‑on knowledge of Chain‑of‑Thoughts, Tree‑of‑Thoughts, Graph‑of‑Thoughts prompting strategies.
• Proficiency with GenAI frameworks/tools and technologies such as Apache Airflow, Spark, Flink, Kafka/Kinesis, Snowflake, and Databricks.
• Experience building conversational AI (text, voice), content generation, or code generation systems.

Experience

• Hands‑on expertise in scaling and maintaining production‑grade ML services, focusing on ML/LLM Operations (versioning, automation, observability, automated training and monitoring).
• Experience partnering with engineering, product, business operations, legal and other teams while designing, building, and executing solutions.
• Deep understanding of at least three of the following areas: data mining, advanced statistics, machine learning, deep learning (incl. NLP).

Application Details

• The role is hired through an Employer of Record (EoR) partner in India. The candidate will be legally employed in India through the EoR partner and will work full‑time, aligned to Credit Acceptance.
• Day‑to‑day work, responsibilities, and performance expectations will be consistent with the global team members, with local compliant payroll, benefits, and statutory coverage through the EoR partner.

Compensation

CTC Range: ₹63,55,839 – ₹93,21,897. Final CTC will be shared at the offer stage and include all compensation components. It is influenced by role‑specific skills, depth and experience level, industry background, relevant education and certifications.

Company Values

• Positive: maintaining resiliency and focusing on solutions.
• Respectful: collaborating and actively listening.
• Insightful: cultivating innovation, acquiring business and role‑specific knowledge, demonstrating self‑awareness, and making quality decisions.
• Direct: effectively communicating and conveying courage.
• Earnest: taking accountability, applying feedback, and effectively planning and prioritizing.

Expectations

• Regularly overlap with U.S. business hours to support collaboration with global team members.
• Remain compliant with company policies, processes, and guidelines.
• All other duties as assigned.
• Attendance as required by department.