Java Tech Lead: Cloud Microservices & CI/CD Leader

TechDigital Group; Orlando (FL); On-site; USD 90,000 - 150,000; Full time

Job summary

An established industry player is seeking a highly experienced Java Developer with over 13 years of expertise in Java8, Spring Boot, and Microservices. This role involves managing multiple projects, ensuring production stability, and collaborating with cross-functional teams to deliver high-quality software solutions. The ideal candidate will have a strong background in Agile methodologies and excellent communication skills. Join this dynamic team to drive innovation and deliver impactful projects in a supportive environment that values your contributions and professional growth.

Qualifications

• 13+ years of experience in Java8 and related technologies.
• Strong understanding of Agile methodologies and project management.

Responsibilities

• Manage project planning, scheduling, and resource coordination.
• Ensure production stability and coordinate with DevOps teams.

Skills

Java8

Spring Boot

Hibernate

Microservices

GCP

AWS

Agile Methodology

Software Design Patterns

Communication Skills

Job description

Key Responsibilities

For the AI Engineer, required skillset are: Databricks, Python (pandas, scikit-learn, NumPy, TensorFlow, MLflow) SQL, and Agentic AI frameworks (LangChain, LangGraph, OpenAI, MCP)

Responsibilities

• Develop machine learning, deep learning, and generative AI models to support RCM use cases such as claim outcome prediction, denials classification, next best action recommendation, clinical appeal summarization, and workflow optimization.

• Build end to end ML pipelines including feature engineering, model training, hyperparameter tuning, validation, and monitoring.

• Research and apply advanced techniques in LLMs, embeddings, retrieval augmentation (RAG), prompt engineering, and document intelligence.

• Translate operational and product requirements into measurable model objectives, data specifications, and evaluation frameworks.

• Conduct exploratory data analysis (EDA) to understand data patterns, anomalies, and business insights.

• Implement and follow best practices around MLOps, including model versioning, reproducibility, feature stores, and drift monitoring.

• Partner with Data Engineers to ensure feature availability, data quality, and scalable ML/AI deployment within Azure Databricks and enterprise platforms.

• Collaborate with cross-functional teams to communicate insights, present model results, and drive adoption of ML solutions.

• Maintain awareness of emerging AI technologies and propose enhancements or new opportunities for Provider Engineering platforms.

Required Qualifications

• Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Applied Mathematics, or a related quantitative field.

• 3–7+ years of hands-on experience developing ML or AI models in an applied industry setting (healthcare experience strongly preferred).

• Strong proficiency in:

• Databricks
• Python (pandas, scikit-learn, NumPy, TensorFlow, MLflow)
• SQL

• Preferred: Knowledge of US healthcare, ideally with Revenue Cycle Management (RCM) experience, including familiarity with EDI transactions such as 837 and 835.

• Ability to thrive in a fast-paced environment with rapid prototyping, ambiguity, and iterative delivery.

• Required: Strong working knowledge of GitHub Copilot (or equivalent enterprise-approved AI coding assistant) to accelerate development.

• Self-motivated and solution-oriented; able to independently research, troubleshoot, and identify paths forward when facing technical or data challenges.

• Experience working in Azure or similar cloud environments:

Azure Databricks (MLflow, Delta Lake)

Azure Machine Learning

Azure Data Lake / Azure Data Factory (in partnership with Data Engineering)

• Hands-on experience in at least one of the following:

Classification, regression, time series, or recommendation systems

Agentic AI / Large Language Models (LLMs) / RAG

• Strong statistical modeling foundation and experience building production-quality ML systems.

• Ability to communicate technical findings to nontechnical stakeholders clearly and effectively