Machine Learning Engineer

General Purpose We are looking for a highly skilled Machine Learning Engineer to join our team and contribute to the development of AI-powered solutions. The candidate should have expertise in Python programming, REST API development, cloud infrastructure, and AI model integration. This role requires strong problem-solving skills, a deep understanding of AI technologies, and the ability to optimize and deploy AI applications efficiently. The ideal candidate will have hands-on experience in building, deploying, and maintaining machine learning models and Gen AI solutions using modern cloud platforms and MLOps practices. You will work closely with cross-functional teams to design scalable AI pipelines and integrate LLM-based solutions into production environments. You will play a critical role in building scalable, resilient, and secure data platforms that power analytics, AI, and data-driven innovation at Wynn.Nature & Scope Essential Duties & Tasks Design, implement, and maintain end-to-end ML pipelines using Databricks notebooks, Delta Lake, and MLflow for experiment tracking, model versioning, and lifecycle management.Leverage Databricks Auto ML for rapid prototyping and efficient model selection in early-stage experimentation.Implement ML model deployment workflows using Databricks Jobs and manage serving endpoints with low-latency requirements.Design, develop, and deploy ML and Gen AI solutions using Python and LLM frameworks.Build and deploy ML production systems, contributing to their design and ongoing maintenance Develop and maintain ML pipelines, manage the data lifecycle, and ensure data quality and consistency throughout Assure robust implementation of ML guardrails and manage all aspects of service monitoring Develop and deploy accessible endpoints, including web applications and REST APIs, while maintaining steadfast data privacy and adherence to security best practices and regulations Embrace agile development practices, valuing constant iteration, improvement, and effective problem-solving in complex and ambiguous scenarios Collaborate with cross-functional teams including architects, data engineers, analysts, and business leaders to deliver robust data solutions.Write and maintain infrastructure as code using YAML, Implement and manage CI/CD pipelines for ML workflows.Ensure model governance and compliance using protocols like Model Context Protocol and A2A Protocol.Produce comprehensive documentation including architectural diagrams, data flow maps, runbooks, and lineage tracking artifacts.Continuously improve ml pipeline efficiency by identifying performance bottlenecks and reducing compute/storage costs.Containerize applications using Docker and manage deployments on Azure Implement Dev Ops best practices in the data lifecycle, including CI/CD for pipelines, automated testing, and version control.Facilitate internal knowledge sharing via technical workshops, peer reviews, and training sessions for continuous team development.Education A bachelor's degree in computer science, information technology, or a related field is required; a master's degree is preferred but not mandatory.Minimum age 21.Experience A minimum of 3-5 years of hands-on experience in Python software development with a focus on modular, scalable, and efficient coding for AI/LLM-based applications.Proven track record of building, scaling, and leading complex ML engineering platforms in enterprise environments.Strong experience designing and deploying cloud-native ML solutions on Azure Databricks, with an emphasis on scalable model training, model management using MLflow, and real-time inference.Demonstrated success in implementing AI agents or autonomous workflows using tools such as Lang Graph, Crew AI, or similar frameworks.Experience in real-time or near-real-time inference systems and low-latency model serving.Skills / Knowledge Hands-on experience with Databricks Machine Learning environment, including MLflow tracking, model registry, and production deployment workflows.Proficient in using Databricks Feature Store, Auto ML, and real-time model inference using Databricks Serving or external endpoints.Familiarity with Databricks Unity Catalog and access control for secure ML asset management.Advanced proficiency in Python and LLM, with strong skills in performance tuning, modular coding, and automation scripting Experience deploying Gen AI & related frameworks (e.g., Lang Chain, LLMOps, Pa LM) use cases in Snowflake ecosystem Develop, optimize, and maintain scalable machine learning workflows and models using Databricks notebooks and MLflow.Understanding of how MCPs can complement structured Snowflake data to deliver rich narrative insights, automated clinical summaries, and patient engagement tools.Build AI-driven applications using Snowflake Cortex, Document AI, and Snowpark ML.Integrate Gen AI tools like Open AI GPT, Claude (Anthropic), and x AI’s Grok to enhance unstructured data processing and generate intelligent summaries or decision recommendations.Experience in experimentation, feature engineering, Model registration, deployment, and monitoring, using Auto ML cost and quota management Strong written and verbal communication skills in English, with the ability to influence, mentor, and align technical teams and stakeholders.Candidates with coursework or academic research in deep learning, reinforcement learning, or natural language processing are highly encouraged.Certifications Required/Preferred Databricks Certified Machine Learning Professional Snowflake Snow Pro Core Certification Azure Data Scientist Associate Work Conditions This is an office-based position with regular working hours #J-18808-Ljbffr