AI Engineer / Data Scientist 3 key skills 1. Machine Learning & LLM/GenAI Development Strong ability to build and optimize ML models, deep learning pipelines, and GenAI/LLM solutions (including embeddings, vector stores, RAG workflows, and NLP applications). 2. Data Engineering & Pipeline Development Expertise in handling large datasets, performing feature engineering, and building reliable training/inference pipelines using Python, SQL, and cloud data platforms. 3. AI Engineering & Production Deployment (MLOps) Capability to convert models into production services, deploy using cloud platforms (Azure/AWS/GCP), implement CI/CD, monitoring, and operationalize models in scalable, secure environments. Job Description: AI Engineer / Data Scientist Role Summary The AI Engineer / Data Scientist is responsible for designing, developing, and deploying AI/ML models and intelligent applications that solve complex business and clinical problems. This role works across data science, engineering, and product teams to build scalable machine learning pipelines, develop LLM/GenAI solutions, and operationalize models into production environments. Experience in healthcare analytics, clinical workflows, or operational intelligence is strongly preferred. ________________________________________ Key Responsibilities AI/ML Model Development • Build machine learning, deep learning, and statistical models for prediction, classification, optimization, and NLP tasks. • Develop and fine tune LLM based applications, prompt workflows, embeddings, RAG pipelines, and domain specific AI solutions. • Conduct exploratory data analysis (EDA), feature engineering, hypothesis testing, and model evaluation. Data Engineering & Pipeline Development • Work with large, complex datasets using SQL/NoSQL, distributed compute, and cloud data platforms. • Build reusable data pipelines for training, inference, and monitoring using Python, PySpark, or similar tools. • Ensure data quality, lineage, governance, and compliance (HIPAA/PHI when applicable). AI Engineering & Deployment • Convert models into production-grade services using microservices, APIs, and containerized deployments. • Implement model deployment, MLOps workflows, CI/CD pipelines, and automated monitoring (drift, performance, accuracy). • Collaborate with engineering teams to integrate AI capabilities into enterprise platforms and digital solutions. Collaboration & Stakeholder Engagement • Partner with product, architecture, business SMEs, and clinical stakeholders to define requirements and success metrics. • Present insights, models, and results to diverse audiences in a clear and actionable manner. • Contribute to reusable components, frameworks, and best practices for enterprise-wide AI adoption. ________________________________________ Required Qualifications • Bachelor’s or Master’s in Computer Science, Data Science, AI/ML, Engineering, Statistics, or related field. • 3–6+ years of experience in data science, machine learning, AI engineering, or applied analytics. • Strong proficiency in Python (NumPy, Pandas, Scikit-learn, PyTorch/TensorFlow). • Experience with NLP, LLMs, vector databases, RAG workflows, or GenAI applications. • Hands-on experience with cloud platforms (Azure, AWS, or GCP) and MLOps/tooling (Azure ML, Databricks, MLflow, etc.). • Strong SQL skills and data manipulation expertise. ________________________________________ Preferred Qualifications • Experience in healthcare analytics, clinical data (FHIR/HL7), RCM, SCM, or patient-engagement solutions. • Knowledge of Azure AI services, Azure OpenAI, Microsoft AI Foundry, or similar AI platforms. • Exposure to microservices, API design, Docker, Kubernetes, or CI/CD pipelines. • Understanding of Responsible AI, data privacy, HIPAA, and regulated environments. ________________________________________ Key Attributes • Strong analytical thinking and problem-solving abilities. • Ability to convert ambiguous requirements into working AI solutions. • Collaborative, curious, and passionate about applied AI. • Excellent communication skills and ability to work with cross-functional teams.
