Senior AI or ML Engineer (8+)
unitedhealthgroup
|
14 days ago
|
Noida
- Conversational AI and Bot Development
- Design, develop, test, deploy, and continuously improve enterprise-grade chatbots, voice bots, virtual assistants, and copilots
- Build multi-turn conversational experiences incorporating intent recognition, entity extraction, dialogue and session management, context retention, disambiguation, personalisation, and task completion
- Develop conversational journeys across chat, voice, web, mobile, contact-centre, and enterprise collaboration channels
- Implement handling for interruptions, corrections, ambiguous requests, incomplete information, fallbacks, escalation, and human-agent hand-off
- Integrate bots with enterprise APIs, knowledge repositories, workflow platforms, identity services, contact-centre solutions, and backend applications
- Apply conversation-design principles to deliver natural, accessible, consistent, and task-oriented user experiences
- Agentic AI Solution Design and Implementation
- Design and build agentic bots capable of reasoning, planning, function calling, tool usage, memory management, knowledge retrieval, API interaction, and multi-step workflow execution
- Develop single-agent and multi-agent solutions using frameworks such as Google ADK, LangGraph, LangChain, Semantic Kernel, AutoGen, CrewAI, or similar technologies
- Implement orchestration patterns including supervisor, router, planner-executor, specialist-agent, sequential, parallel, and human-in-the-loop workflows
- Define agent instructions, tools, state, memory, permissions, and operational boundaries to support predictable and secure behaviour
- Implement approval checkpoints, restricted tool access, retry and timeout policies, action validation, auditability, and safe termination
- Build graceful fallback and escalation mechanisms when an agent lacks sufficient information, confidence, permissions, or capability
- Google Conversational AI and Agent Ecosystem
- Design and optimise conversational experiences using Google GECX, including flows, intents, entities, routes, fulfilment, parameters, events, integrations, and environment configuration
- Develop and orchestrate AI agents using Google ADK, including agent definitions, tools, sessions, state, memory, workflow coordination, and evaluation
- Integrate Google conversational AI solutions with Gemini models, enterprise knowledge sources, APIs, webhook services, and backend applications
- Develop reusable bot components, agent templates, tools, connectors, conversation patterns, prompts, and evaluation assets
- Troubleshoot issues across conversation flows, agent orchestration, fulfilment services, session state, authentication, APIs, channels, and production telemetry
- Hands-On Engineering and Cloud Development
- Contribute directly to bot configuration, software development, prompt engineering, agent orchestration, integration, testing, debugging, and production delivery
- Build fulfilment services, APIs, microservices, reusable libraries, and connectors using Python, Java, Spring Boot, or comparable technologies
- Integrate bots and agents with REST APIs, databases, event streams, workflow engines, identity providers, CRM platforms, claims systems, and knowledge repositories
- Build secure, scalable, cloud-native AI solutions, preferably using Google Cloud, while supporting Azure-based environments where required
- Apply modular design, source control, automated testing, code reviews, observability, documentation, and secure development practices
- Ensure solutions meet enterprise requirements for reliability, privacy, security, scalability, resiliency, and operational readiness
- DevOps, Governance and Operational Readiness
- Build CI/CD pipelines for bot configurations, prompts, agent definitions, orchestration code, fulfilment services, infrastructure, and evaluation assets
- Support containerised deployments using Docker, Kubernetes, Google Kubernetes Engine, Azure Kubernetes Service, or comparable platforms
- Implement quality gates, security scanning, configuration validation, environment promotion, monitoring, rollback, and release governance
- Establish logging, distributed tracing, dashboards, alerting, operational runbooks, incident triage, and recovery procedures
- Apply DevOps, MLOps, and LLMOps practices to support controlled and continuous improvement
- Implement authentication, authorisation, least-privilege access, secrets management, data protection, safe tool execution, and auditability
- Ensure alignment with enterprise security, privacy, compliance, and Responsible AI requirements
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