Primary Responsibilities:

- Architect, develop, and deploy large-scale AI and data-driven applications using Python and relevant frameworks.
- Lead the design and implementation of agentic AI workflows, prompt engineering, and retrieval augmented generation (RAG) patterns.
- Integrate generative AI solutions with enterprise databases (PostgreSQL, Oracle, Vector DBs like ChromaDB) and cloud platforms.
- Build and optimize CI/CD pipelines (GitHub Actions, Jenkins) for production-scale deployment and reliability.
- Collaborate with cross-functional teams to deliver AI-powered features, including natural language querying, data comparison, and visualization.
- Develop automation scripts and utilities to streamline data processing, archiving, and system maintenance.
- Establish and enforce best practices in code review, testing, and documentation for AI and data engineering projects.
- Participate in customer presentations, handle critical issues, and provide technical insights on AI solutions.

AI Builder Responsibilities

Use approved GenAI tools, including Copilot or equivalent enterprise-authorized tools, to accelerate ideation, code generation, test generation, documentation, debugging, and productivity improvements.

Apply AI-assisted development techniques responsibly to improve code clarity, maintainability, testability, and engineering flow while validating outputs before use.

Develop scripts, utilities, workflow automations, and internal productivity accelerators that reduce repetitive work and improve engineering effectiveness.

Follow responsible AI, privacy, security, intellectual property, and data handling expectations when using AI tools.

Stay current with emerging GenAI engineering practices,



prompt patterns, agentic workflows, and AI-enabled SDLC opportunities relevant to assigned products.

Builder Responsibilities

Design, develop, and deploy AI-enabled or automation-enabled solutions using approved no-code, low-code, and advanced engineering platforms where they create measurable value.

Translate business needs into fit-for-purpose technical solutions that improve product capabilities, workflows, decision support, operational efficiency, or developer productivity.

Prototype quickly, validate assumptions with stakeholders, and convert successful prototypes into maintainable, secure, and scalable solutions.

Design and implement automated claims testing solutions, leveraging Python and AI to validate claims processing workflows and ensure accuracy.

Develop and maintain test automation frameworks for claims-related systems, integrating with CI/CD pipelines for continuous testing.

Collaborate with QA and business analysts to define test cases, automate regression testing, and monitor test coverage.

Analyze claims data using AI and machine learning techniques to identify anomalies, optimize processing, and improve system reliability.

Provide technical leadership in claims testing automation, mentoring team members and driving process improvements.

Required Qualifications





Bachelor's degree, graduate degree, or equivalent practical experience in computer science, engineering, information technology, or a related field.

4+ years of hands-on experience in Python software engineering, with a solid focus on AI and data solutions.

Deep knowledge of generative AI frameworks: LLM orchestration, agentic AI, prompt engineering, and RAG patterns.

Experience with modern web frameworks.

Proficiency in database integration: PostgreSQL, Oracle, Vector DBs (ChromaDB).

Expertise in DevOps tools: GitHub Actions, Jenkins, Airflow, CI/CD pipelines.

Familiarity with cloud deployment and Kubernetes-hosted applications.

Strong background in automation, data pipelines, and microservice architecture.

Demonstrated ability to deliver AI-powered web applications.

Preferred Qualifications

Foundational experience with cloud platforms such as AWS, Azure, or GCP, including deployment, configuration, or operational support of cloud-hosted applications.

Experience with healthcare claims, benefits, authorization, or clinical platforms.

Experience with Facets, CSP, or related healthcare modernization platforms.

Experience with container platforms and orchestration such as Docker, Kubernetes

Experience with OpenCV, Keras, TensorFlow, and other ML libraries

Experience with AI-assisted SDLC practices, GitHub Copilot, Microsoft Copilot, prompt engineering, test generation, code explanation, or productivity automation.

Experience creating technical documentation such as design notes, API specifications, troubleshooting guides, runbooks, or operational readiness checklists.

📌 Ai Ml Engineer (Noida)
🏢 Optum
📍 Noida