Lead AI Engineer

About AGS Health
AGS Health is a leading strategic growth partner to healthcare providers across the U.S. Working alongside each client as one team, we improve revenue cycle performance by orchestrating data, AI, automation, and human expertise across every workflow. Our proven model complements customers' existing teams, technology, and processes, deploying the right capabilities to improve efficiency, strengthen financial performance, and enhance the patient financial experience. Supported by a global team of more than 16,000 clinical and revenue cycle experts across onshore, nearshore, and offshore locations, AGS Health helps customers achieve measurable results across diverse care settings and specialties.
Job Title: AI Lead Engineer
Location: [Bangalore]
Job Overview
The AI Lead will be responsible for providing technical leadership, AI architecture, model strategy, engineering direction, and mentorship across a team of AI/ML engineers and data scientists. The role requires a strong combination of hands-on AI expertise, production engineering experience, GenAI/LLM knowledge, and the ability to translate complex business problems into scalable AI solutions.
The candidate should have experience taking AI solutions from experimentation and prototyping through production deployment, with a strong focus on accuracy, scalability, reliability, explainability, and measurable business outcomes.
Key Responsibilities
AI/ML Technical Leadership
Lead the technical design and development of AI/ML solutions for medical coding and RCM related healthcare workflows.
Define AI/ML architecture, model strategy, experimentation methodology, and technical standards.
Guide the team in selecting appropriate approaches across traditional ML, deep learning, NLP, Transformers, LLMs, RAG, and hybrid AI architectures.
Review model performance, experimentation results, error analysis, and improvement strategies.
Establish best practices for model development, evaluation, validation, deployment, and monitoring.
Drive continuous improvement in model accuracy, coverage, latency, scalability, and cost efficiency.
AI & GenAI Architecture
Design scalable AI/ML and Generative AI architectures for enterprise production environments.
Lead development of solutions using LLMs, Transformers, RAG, embeddings, vector databases, fine-tuning, LoRA/PEFT, and agentic AI where appropriate.
Evaluate foundation models and determine when to use prompting, RAG, fine-tuning, traditional ML, or hybrid approaches.
Establish frameworks for LLM evaluation, hallucination mitigation, guardrails, grounding, and response quality.
Develop reusable AI capabilities and platforms that can support multiple coding and healthcare use cases.
Production AI & MLOps
Drive the transition of AI/ML models from experimentation to reliable production services.
Define model deployment and serving architecture using cloud-native technologies.
Establish CI/CD, model versioning, experiment tracking, monitoring, and rollback practices.
Optimize AI inference performance through batching, caching, quantization, model optimization, GPU utilization, and appropriate infrastructure selection.
Design solutions for high availability, scalability, fault tolerance, and production reliability.
Monitor model performance, data drift, model degradation, latency, infrastructure utilization, and operational costs.
Partner with engineering and cloud teams to optimize AI infrastructure and cloud costs.
Technical Leadership & Team Development
Mentor and develop AI/ML engineers and data scientists.
Provide technical guidance on complex modeling, architecture, experimentation, and production challenges.
Establish engineering and AI development standards across the team.
Encourage knowledge sharing, peer reviews, technical documentation, and continuous learning.
Break down complex AI initiatives into technical workstreams and effectively delegate ownership.
Qualifications
Educational Background
Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related technical field.
Required Skills and Competencies
7+ years of experience in AI/ML, Data Science, Machine Learning Engineering, or related fields.
Strong hands-on programming experience in Python.
Strong understanding of Machine Learning and Deep Learning concepts and algorithms.
Hands-on experience with PyTorch, TensorFlow, or similar frameworks.
Strong knowledge of NLP, Transformers, and modern AI architectures.
Hands-on experience with Generative AI and Large Language Models (LLMs).
Experience with RAG, embeddings, vector databases, prompt engineering, and LLM evaluation.
Experience with LLM fine-tuning or customization, such as LoRA, PEFT, or QLoRA.
Experience designing and deploying AI/ML solutions in production.
Strong understanding of MLOps, model deployment, monitoring, and lifecycle management.
Experience with cloud platforms such as AWS, Azure, or GCP.
Strong understanding of software engineering principles, APIs, microservices, Git, testing, and CI/CD.
Strong problem-solving, communication, and technical leadership skills
Preferred Qualifications
Experience with AWS SageMaker, Bedrock, Azure ML, Vertex AI, or equivalent AI platforms.
Experience with Docker and Kubernetes.
Experience with MLflow, Kubeflow, or similar MLOps platforms.
Experience with LangChain, LlamaIndex, DSPy, or similar AI frameworks.
Experience with agentic AI and AI orchestration.
Experience optimizing AI/LLM inference for latency, throughput, and cost.
Knowledge of model quantization, distillation, batching, caching, and GPU optimization.
Experience working with large-scale datasets and distributed data processing.
Experience building AI platforms or reusable AI/ML components.
Experience leading or mentoring AI/ML engineers.
Experience working in a fast-paced product or technology environment.

Work arrangement
No