• Help design, build, and ship GenAI features — RAG pipelines, LLM-powered applications, and conversational interfaces — across our products. • Take ambiguous problems, break them into approaches, prototype quickly, and iterate based on what you learn. • Work with LLM APIs and AWS services (Bedrock, Lambda) to wire up prototypes, pipelines, and integrations. • Build and consume APIs to connect models to data, databases, and backend systems. • Test, evaluate, and debug AI behavior — prompts, retrieval quality, edge cases — to make sure things actually work. • Document what you build and share what you learn with the team. • Keep up with a fast-moving field and bring ideas back to us.

Role Description - Design, develop, and maintain ESB integration solutions. - Develop and support APIs, web services, and enterprise integrations. - Build and maintain REST and SOAP services. - Integrate enterprise applications, databases, and third-party systems. - Troubleshoot and resolve integration-related issues. - Perform unit testing, system integration testing, and production support. - Collaborate with Business Analysts, Solution Architects, and Development teams to gather integration requirements. - Ensure integration solutions follow security, scalability, and performance best practices. - Prepare technical documentation and integration specifications. - Participate in code reviews and technical design sessions. Qualifications - 5+ years of related experience developing and implementing solutions using Middleware. - In-depth working experience developing applications using different programming languages used in ESB. - In-depth knowledge of standards/technologies (JMS, XML, JSON, WSDL, SOAP, REST, HTTP, and SSL/TLS). - Experience with different types of Integration Patterns. - Sound knowledge of OOP. - Good knowledge of multiple mapping tools. - Stakeholder communication by translating complex technical integration concepts into clear, actionable business insights, and vice versa. Requirements - Define integration test scenarios, facilitate User Acceptance Testing (UAT), and help validate that integrated systems meet business and data integrity standards. - Work alongside Integration Engineers to define API contracts, payload structures (JSON/XML), and endpoints using tools like Postman or Swagger/OpenAPI.

• Design, build, and deploy end-to-end AI and machine learning solutions, with a focus on GenAI, NLP, and healthcare applications. • Develop and productionize LLM-based workflows, including prompt engineering, evaluation frameworks, fine-tuning approaches, and Retrieval-Augmented Generation systems. • Translate ambiguous business and healthcare problems into structured data science solutions with clear success metrics. • Own the full model lifecycle, including data preparation, experimentation, validation, documentation and articulation of results, deployment, monitoring, and continuous improvement following RAI guidelines. • Work with large-scale structured and unstructured data, including clinical, operational, claims, member, provider, or other healthcare-related datasets. • Partner with product, engineering, business, clinical, and compliance stakeholders to ensure solutions are scalable, explainable, secure, and aligned with business needs. • Lead, mentor, and develop a team of data scientists, and AI engineers, setting high standards for technical quality, analytical rigor, and delivery discipline. • Drive best practices in model development, code quality, documentation, reproducibility, and responsible AI.

• Lead end-to-end architecture for data platforms and pipelines: scraping, data extraction, transformation, storage, serving, and ML/LLM integration, balancing performance, reliability, security, and cost. • Incrementally scale pipelines and systems: design safe rollout plans and north star data-quality metrics to handle customer and traffic growth without impacting production. • Translate business goals into actionable data products: assess high-level requirements, carve clear problem spaces, draft crisp RFCs, and sequence work into deliverable projects for the team. • Establish and enforce engineering standards: testing strategy, evals, observability, data contracts, and security practices across services. Think through short-term and long-term goals to come up with fast go-to-market products, while planning ahead for productization. • Up‑level the org: lead architecture reviews, codify patterns, mentor Senior Engineers, and multiply impact through documentation, code reviews, and pairing. • Startup‑ready: flexible, comfortable with ambiguity and constant change; proactive about process, documentation, and reliability without over‑engineering. • Lead the collaboration and define how AI engineers work cross-functionally with software engineers, devops, product managers and designers, to conceptualize and shape innovative and impactful solutions. Provide mentorship to junior team members and cultivate a culture of collaboration and innovation. • Ship meaningful experiments: prototype data/ML capabilities, evaluate feasibility and ROI, and make pragmatic calls on productionalizing with an eye on operating costs and risk.