Senior AIML Engineer

Role & responsibilities

Primary Responsibilities:

• Lead the design, development, testing, deployment, maintenance, and continuous improvement of AI/ML solutions, translating models and algorithms into scalable, secure, reliable software used in enterprise systems
• Serve as the technical lead for complex AI/ML initiatives, owning end-to-end implementation from solution design through production deployment, monitoring, operational support, and ongoing optimization
• Build responsible AI solutions and reusable service components that comply with enterprise security, privacy, quality, ethical, regulatory, and responsible AI standards
• Develop AI/ML-enabled features and capabilities, including Generative AI, predictive analytics, automation, chatbots, conversational AI, and other intelligent workflow solutions
• Champion AI/MLOps best practices to ensure models, pipelines, services, and orchestration layers are production-ready, monitored, observable, resilient, and continuously improved
• Drive modern engineering practices across teams, including automation-first development, code reuse, observability, test automation, CI/CD, operational excellence, and AI DLC adoption for AI systems
• Partner with product management, architects, engineering leadership, and broader technology teams to deliver creative, scalable AI solutions aligned to business objectives, enterprise AI strategy, and engineering best practices
• Create and maintain comprehensive solution architecture, integration, and network architecture diagrams for AI/ML solutions and related service components
• Enforce and promote enterprise security, privacy, compliance, and quality standards within AI/ML solutions, proactively identifying and mitigating technical, operational, data, and model-related risks
• Accountable for high-quality software deliverables, including maintainable code, reliable services, clear documentation, production readiness, and measurable operational outcomes
• Mentor, guide, and train engineers and team members on AI/ML technologies, engineering patterns, frameworks, MLOps, and responsible AI practices
• Use enterprise-approved AI tools to streamline engineering workflows, automate repeatable tasks, improve delivery efficiency, and support continuous improvement across teams
• Collaborate with cloud, platform, and modernization teams to integrate AI capabilities into cloud-native, digital-first, and enterprise-scale technology solutions
• Exercise independent judgment to prioritize, plan, and manage AI-related engineering activities, dependencies, risks, and delivery commitments
• Communicate complex AI/ML concepts, solution options, technical tradeoffs, risks, and results clearly to technical teams, business stakeholders, and senior leadership
• Evaluate emerging AI/ML technologies, frameworks, patterns, and industry trends to inform solution design, optimize architecture, and drive practical enterprise innovation
  
• Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regards to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so

Qualifications - External

Required Qualifications:

• Bachelor's degree in Computer Science or related technical field
• 7+ years of software engineering experience and using Python, Java, Scala, or similar programming languages
• 5+ years of hands-on experience building AI/ML solutions, including LLMs, Generative AI, and traditional ML models, Design, build, and deploy LLM powered solutions, including RAG pipelines, agentic workflows, decisioning agents, and orchestration layers
• Experience in the healthcare industry
• Experience or knowldge in Architect cloud native solutions on Azure
• Full-stack engineering experience
• Solid experience with Chatbots, conversational AI, and NLP-based solutions
• Solid knowledge of security, access controls, logging, monitoring, performance tuning, disaster recovery, and production operations
• Proven ability to design and build end to end AI services including backend APIs, orchestration services, and UI integrations for claims users and operations teams
• Proven ability to develop scalable backend services using Java (Spring Boot) and modern API frameworks
• Proven ability to build and integrate web applications using modern frontend frameworks (React or equivalent) where needed for AI driven user experiences
• Proven ability to develop AI components leveraging Azure public cloud, foundation models, embeddings, vector stores, and prompt engineering best practices, Solid experience, with deep backend expertise in Python, Java and REST/GraphQL APIs
• Proven ability to implement agent based architectures (AgentCore / agentic cloud patterns) to support complex claims workflows such as adjudication assistance, exception handling, and HITL (human in the loop) scenarios
• Proven ability to continuously evaluate model performance, hallucination risks, explainability, and operational metrics in production