• Develop tabular ML models for operational decision-making: risk classification (XGBoost), dynamic pricing (gradient boosting), and combinatorial assignment optimization (Google OR-Tools). • Operate models in shadow mode with human-in-the-loop (HITL) validation before any automation. • Build the MLOps pipeline: training in Azure ML, model registry in MLflow, inference deployment (FastAPI/Docker) on AWS, with environment promotion and rollback. • Define metrics and backtesting with point-in-time correctness using the Feature Store. • Implement drift monitoring, automated retraining, and canary/A-B deployment via CI/CD. • Serve models via low-latency inference APIs (online features in Redis), with OpenAPI contracts.

• Work on Data Engineering activities with a focus on Machine Learning; • Work on modeling and structuring data layers; • Build analytical views for use in AI solutions; • Work with RAG (Retrieval-Augmented Generation) and LLMs; • Develop solutions for analytical and artificial intelligence environments.

Role Description Join our innovative team and help us in shaping the future of Technology! - Design, develop, and deploy AI/ML solutions to modernize core pharmacy platforms, with a focus on scalability, reliability, performance, and security. - Leverage Generative AI, LLMs, and agentic AI frameworks to automate and enhance pharmacy workflows and decision-making processes. - Collaborate with business and technical stakeholders to understand pharmacy domain requirements and translate them into robust AI/ML-driven technical solutions. - Contribute to architecture and technical design of AI/ML pipelines, including model selection, data integration, and deployment patterns. - Establish and maintain AI/ML engineering standards, best practices, and quality assurance processes. - Conduct code reviews, provide constructive feedback, and mentor engineers on modern AI/ML engineering practices. - Partner with cross-functional teams including data scientists, product managers, platform engineers, and pharmacy domain experts. - Translate legacy system modernization needs into scalable AI applications that enhance products, workflows, and operational efficiency. - Monitor and optimize AI/ML model performance, resource utilization, and platform reliability. - Collaborate with DevOps and infrastructure teams to ensure secure, scalable deployment and operations of AI/ML solutions. - Stay current with advancements in AI/ML frameworks, Generative AI, LLMs, and pharmacy technology modernization. Qualifications - 5+ years of experience as a software engineer or AI/ML engineer delivering cloud-based solutions. - 5+ years of experience with cloud platforms such as Azure, AWS, or Google Cloud. - 5+ years of programming experience in Python, Golang, JavaScript, or Java. - 3+ years of hands-on experience with AI/ML frameworks, model development, and deployment. - 3+ years of hands-on experience with Generative AI, LLMs, and agentic AI solutions. - Experience building distributed systems and cloud-native AI/ML applications. - 1+ years of experience with security and compliance in cloud environments. - Strong problem-solving and analytical skills with the ability to propose innovative AI/ML solutions. - Excellent communication and collaboration skills across technical and non-technical teams. Company Description VXForward is a leading technology solutions provider that helps to create a business value with innovative and cost-effective services across different industries. - Exciting Projects, Meaningful Impact - Continuous Learning and Growth - State-of-the-Art Technologies - Collaborative Team Environment - Make a Real Difference - Work-Life Balance

• Understanding the client’s business use cases and technical requirements and be able to convert them into technical design which elegantly meets the requirements. • Mapping decisions with requirements and be able to translate the same to developers. • Identifying different solutions and being able to narrow down the best option that meets the client’s requirements. • Defining guidelines and benchmarks for NFR considerations during project implementation • Writing and reviewing design document explaining overall architecture, framework, and high-level design of the application for the developers • Reviewing architecture and design on various aspects like extensibility, scalability, security, design patterns, user experience, NFRs, etc., and ensure that all relevant best practices are followed. • Developing and designing the overall solution for defined functional and non-functional requirements; and defining technologies, patterns, and frameworks to materialize it • Understanding and relating technology integration scenarios and applying these learnings in projects • Resolving issues that are raised during code/review, through exhaustive systematic analysis of the root cause, and being able to justify the decision taken. • Carrying out POCs to make sure that suggested design/technologies meet the requirements.