• Design, build, and optimize ML/DL models for production-scale audio deepfake detection, ensuring robustness across diverse real-world conditions including compression artifacts, noise, telephony, and streaming pipelines. • Partner with clients to develop a deep understanding of their production environments and define model performance criteria. • Investigate failure cases in client environments, build custom evaluation frameworks, and implement mitigation strategies spanning both Engineering and AI. • Design and execute structured experimentation roadmaps aligned with client requirements and proactive system resilience goals. Translate findings into clear and actionable insights. • Monitor, measure, and report on model performance in production using data analytics and AI observability tools (e.g. Datadog, Metabase). Identify degradation trends, data drift, and emerging threat patterns before they impact client outcomes. • Build and maintain dashboards and analytics pipelines that surface model health metrics, enabling data-driven decisions across AI, Engineering, and Product teams. • Collaborate with cross-functional partners — Applied Scientists, Deployment Engineers, and Product teams — to deploy scalable, production-grade models with clear performance benchmarks and monitoring in place.

• Own AI architecture across cloud platforms (AWS Bedrock, Google Vertex AI)—you'll make the calls on what we build and how • Design RAG systems end-to-end, from document ingestion and chunking strategy through retrieval optimization and generation quality • Build agentic systems using modern patterns: MCP (Model Context Protocol), tool-use, function calling, and orchestration frameworks like LangGraph • Define observability and evaluation so we know our AI is actually working in production—not just demo-ing well • Manage cost at scale—model selection tradeoffs, token economics, and budget forecasting as we grow • Establish guardrails and compliance patterns, particularly for healthcare data and HIPAA requirements • Mentor and guide implementation-level developers as the team grows • Ship production systems—this isn't a strategy-only role; you'll write code and deploy real solutions

Role Description RxBenefits is hiring! We are adding a Senior Software Engineer (Software Engineer III) to the growing application development team at our Birmingham, AL headquarters. As a senior engineer, you will be responsible for designing and building the data pipelines, streaming infrastructure, and machine learning systems that power our rapidly growing business. You will also be a thought leader across the technology organization that champions modern data engineering practices. This is an exciting opportunity for a forward-thinking professional who can conceptualize, deliver, and support the data-driven technology that our employees and partners need to succeed. - Design and build real-time data pipelines and streaming infrastructure using Apache Flink and related technologies - Develop and maintain stateful processing layers for fast, reliable data enrichment - Design and build backend services in Golang, Java, or Python that process data reliably at scale - Build, train, and deploy traditional ML models to surface insights and power data-driven features, owning the workflow from feature engineering through production monitoring - Build scalable services that integrate ML model inference, feature stores, and real-time scoring into production workflows - Build tooling and infrastructure for data quality, validation, and pipeline observability - Implement monitoring and observability for data pipelines and ML models, including data quality metrics and drift detection - Ensure solutions meet enterprise standards for reliability, security, compliance, and observability - Partner with product and data teams to translate business problems into ML and data engineering solutions - Participate in architectural design and recommend technical solutions - Review and collaborate with other engineers on their code - Mentor and share knowledge within the team and across the department Qualifications - Bachelor’s degree in computer science, mathematics, engineering or another related field - 6-8 years of professional experience in software development - Strong proficiency in one or more of the following backend languages: Golang, Java, or Python - Solid understanding of RESTful API design, microservices, and distributed systems - Strong foundation in data structures, algorithms, concurrency, and performance optimization - Familiarity with relational and NoSQL databases and their performance characteristics - Hands-on experience (1-3 years) building and evaluating traditional ML models (XGBoost, Random Forests, Logistic Regression) in production environments - Familiarity with the end-to-end ML workflow including feature engineering, model selection, training, evaluation, and production monitoring - Experience handling sensitive data (PII/PHI) in data pipelines, including masking, redaction, or de-identification - Experience building and deploying services on AWS - Experience with Agile development methodologies - Strong communication and presentation skills - Effective working independently or collaboratively within a team - Ability to think strategically and execute with urgency Requirements - Experience building high-throughput data pipelines at scale using Apache Flink, Kafka Streams, or similar frameworks - Experience evaluating ML model performance in production, including data drift and model degradation - Proficiency in AWS services: Sagemaker, Bedrock, MSK, Glue, EMR, DynamoDB, EC2, Lambda, S3, and IAM - RocksDB or similar embedded storage engines - Caching and in-memory database technologies - Asynchronous/multi-threaded programming patterns - Experience working in regulated industries (healthcare, finance, insurance) - Knowledge of governance frameworks around data privacy (HIPAA, SOC2, GDPR, etc.) - Frontend development with NextJS or React Benefits - Remote first work environment - Choice of a HDHP or PPO Medical plan, we pay 100% of the premium for the HDHP for you and your eligible family members - Dental, Vision, Short- and Long-Term Disability, and Group Life Insurance that we also pay 100% of premiums (for your family too on Dental and Vision) - Additional buy-up options for Short- and Long-Term Disability and Life Insurance - 401(k) with an employer match up to 3.5% available after 60 days - Community Service Day to give back and support what you love in your community - 10 company holidays including MLK Day, Juneteenth, and the day after Thanksgiving plus a floating holiday to use as you like - Reimbursements for high-speed internet, we’ll send you a computer and monitors to help you do your best work - Tuition Reimbursement for accredited degree programs - Paid New Parent Leave that can be used for adoption or birth - Pet insurance to protect your furbabies - A robust mental health benefit and EAP service through Spring Health to support you when you need it most

Role Description The GenAI Engineer is a core technical contributor responsible for designing, building, deploying, and managing AI and Machine Learning solutions across enterprise environments. This role focuses on implementing both classical ML and modern Generative AI workloads, including agent-based systems, Retrieval-Augmented Generation (RAG), and LLM-driven pipelines. The engineer ensures all AI solutions are scalable, secure, governed, and aligned with enterprise architecture and operational requirements. Key Responsibilities - Design, build, and deliver end-to-end AI/ML solutions—from experimentation and prototyping to production deployment. - Develop AI solutions using Azure AI Foundry, Azure OpenAI, Azure Machine Learning, and related Azure AI services. - Build agent-based architectures using frameworks such as LangChain, LangGraph, Semantic Kernel, and MCP-style orchestration patterns. - Design and optimize prompt engineering strategies, RAG pipelines, embeddings, vector search, and knowledge-grounding workflows. - Build, train, evaluate, and deploy classical ML and GenAI models using Azure Machine Learning, including pipelines, feature engineering, model registry, and experiment tracking. - Implement MLOps and LLMOps practices including CI/CD, automated testing, responsible deployment, model monitoring, drift detection, and performance optimization. - Integrate AI solutions securely with enterprise systems, APIs, and event-driven architectures. - Embed Responsible AI principles—fairness, explainability, transparency, and human-in-the-loop controls—into solution design and development. - Collaborate closely with Data Engineers, AI Architects, Security teams, and business stakeholders to deliver scalable, compliant AI solutions. - Provide engineering guidance, mentor junior team members, and contribute to reusable components, shared libraries, and engineering best practices. Qualifications - Strong hands-on experience building and deploying AI solutions on Azure, including Azure AI Foundry, Azure OpenAI, Azure Machine Learning, Azure AI Search, and Cognitive Services. - Solid understanding of machine learning concepts including feature engineering, model training, evaluation, hyperparameter tuning, and operational deployment. - Experience deploying both predictive ML and GenAI solutions in enterprise settings. - Hands-on experience with LLM-based system development, agent orchestration, and tool automation using frameworks such as LangChain, LangGraph, Semantic Kernel, and MCP-style agent communication patterns. - Experience implementing RAG pipelines, embeddings, vector databases, and document ingestion architectures. - Strong understanding of LLM constraints, prompt optimization, hallucination mitigation, and output-validation strategies. - Experience implementing CI/CD for ML and LLM workloads, including testing, monitoring, versioning, and automated deployment. - Familiarity with Azure DevOps pipelines, Git-based workflows, and cloud-native deployment automation. - Understanding of cloud-native patterns, containerization, and scalable AI infrastructure. - Knowledge of identity, access management, secrets management, and secure deployment practices for AI systems. - Familiarity with Responsible AI frameworks and enterprise governance models. - Ability to translate business problems into practical, scalable AI solutions. - Strong communication and cross-functional collaboration skills. - Experience working within Agile environments (Scrum, Kanban) delivering iteratively and incrementally. Preferred Certifications & Training - Databricks Certified Generative AI Engineer Associate - Microsoft Azure AI Engineer Associate - Azure Machine Learning Certification - Azure Data Scientist Associate (optional) - MLOps or LLMOps training - LangChain/GenAI specialization coursework Role Impact This role is central to building and scaling enterprise-ready AI capabilities. It enables the development of secure, governed, high-performing AI systems that support organizational innovation, automation, and decision intelligence. Why This Opportunity Is Attractive - Work with cutting-edge AI technologies and modern GenAI frameworks. - Lead hands-on development of AI systems deployed at enterprise scale. - Collaborate with cross-functional experts across architecture, engineering, and security. Why NTT Data? Empowerment and rewards are the cornerstone of our career development model. We are a young, fast-growing company, with a highly innovative and entrepreneurial spirit, because of this professional experience and growth will be unmatched. Our talent and positive attitude allow us to transform our goals into achievements, and projects into realities.