Position Summary:
Precision AQ’s Product Solutions team is expanding its AI and machine learning engineering capabilities in India. This role is responsible for designing, developing, deploying, and scaling production-grade AI/ML solutions that directly support Precision AQ products and productized services across oncology access, analytics, and AI-enabled offerings.
The AI/ML Engineer operates as a hands-on technical contributor, working closely with product management, engineering, data science, and research teams to translate business problems into robust, scalable AI systems. The role spans model development, optimization, deployment, and operationalization, with a strong emphasis on Generative AI, Large Language Models (LLMs), and applied machine learning in real-world production environments.
The successful candidate is technically rigorous, execution-oriented, and comfortable operating in fast-paced, multi-product environments with evolving requirements.
Essential functions of the job include but are not limited to:
- AI / ML Model Development & Optimization
- Design, develop, fine-tune, and evaluate machine learning, deep learning, and Generative AI models, including Large Language Models (LLMs).
- Apply appropriate modeling techniques (supervised, unsupervised, NLP, deep learning) based on problem context and data constraints.
- Optimize model performance across accuracy, latency, scalability, and cost dimensions.
- Conduct rigorous model evaluation, validation, and benchmarking using large-scale datasets.
- Apply data preprocessing, feature engineering, augmentation, and synthetic data generation techniques to improve model robustness.
- Production Deployment & MLOps
- Design and implement scalable, production-ready AI solutions integrated into existing platforms and workflows.
- Build, maintain, and improve MLOps pipelines for model training, deployment, monitoring, and lifecycle management.
- Deploy and manage AI applications in cloud environments (Azure, AWS, or GCP), including containerization and orchestration where applicable.
- Monitor model performance in production; identify drift, degradation, or failures and implement remediation strategies.
- Troubleshoot and resolve AI/ML engineering issues across development and production environments.
- Cross-Functional Collaboration & Integration
- Partner with Product Managers, Product Owners, Software Engineers, Data Scientists, and Research teams to align AI solutions with business and product objectives.
- Translate product requirements and use cases into technical architectures and model designs.
- Support integration of AI capabilities into customer-facing products and internal platforms.
- Communicate technical concepts, tradeoffs, and limitations clearly to non-technical stakeholders.
- Data & Domain Application
- Work with structured and unstructured datasets, including healthcare, claims, and life sciences data, to build high-performance AI systems.
- Ensure responsible handling, transformation, and validation of data used for model training and inference.
- Collaborate with data engineering and QA teams to ensure data pipelines and AI workflows are production-ready and auditable.
- Technical Excellence & Continuous Improvement
- Stay current with advances in Generative AI, LLM architectures, model fine-tuning techniques, and applied machine learning.
- Contribute to internal best practices, standards, and reusable components for AI/ML development.
- Document AI/ML workflows, architectures, methodologies, and lessons learned for internal knowledge sharing.
- Proactively identify opportunities to improve scalability, reliability, and efficiency of existing AI systems.
Qualifications:
Minimum Required:
- Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related quantitative field.
- Minimum 3+ years of hands-on experience in an AI/ML or data science role delivering production-deployed solutions.
- Strong proficiency in Python and SQL; experience building scalable ML/NLP workflows.
- Deep hands-on experience with machine learning, deep learning, and natural language processing.
- Experience working with Generative AI and Large Language Models, including fine-tuning and evaluation techniques.
- Working knowledge of data preprocessing, feature engineering, and model validation practices.
- Experience deploying AI solutions in cloud environments (Azure, AWS, or GCP).
- Familiarity with containerization and orchestration tools (e.g., Docker, Kubernetes).
Preferred:
- Experience building AI solutions in healthcare, life sciences, analytics, or regulated data
- Hands-on experience with MLOps frameworks and monitoring strategies.
- Experience integrating AI models into SaaS products or productized services.
- Familiarity with distributed systems, APIs, and modern software engineering practices.
- Exposure to enterprise AI governance, security, or compliance considerations.