Your future duties and responsibilities
The AI Engineer/Architect will own the architecture and technical direction for enterprise agentic AI solutions, balancing hands-on engineering with design governance. The role will establish reusable patterns for multi agent orchestration, MCP enabled tool ecosystems, secure backend services, integration, observability, and production reliability.
Key Responsibilities
AI Architecture & Multi Agent Orchestration
. Architect and implement robust multi agent orchestration using Python and LangGraph, including intelligent routing, dynamic handoffs, shared state management, long term memory, tool calling, error recovery, and follow up conversation flows.
. Define target and reference architectures for agentic AI, retrieval augmented generation (RAG), model access, memory, evaluation, and human in the loop controls.
. Establish reusable architecture patterns, engineering standards, guardrails, and design review practices for enterprise AI solutions.
MCP & Agent Ecosystem
. Design, develop, and maintain MCP servers using FastAPI and Python to expose tools, resources, prompts, and custom capabilities as part of the agent ecosystem.
. Define standards for tool contracts, discovery, versioning, permissions, schema validation, error handling, and safe execution.
. Build and maintain custom tools and integrations that connect AI agents with internal APIs, enterprise data sources, and legacy systems.
AI Powered Products & Domain Solutions
. Design complex AI powered chatbots and insights engines for critical healthcare and pharmacy benefit management workflows, including claims, pharmacy search, drug coverage, and prior authorization.
. Translate business and product requirements into scalable technical designs and guide solutions from proof of concept through production implementation.
. Select appropriate model, RAG, memory, orchestration, and tool use patterns based on quality, latency, cost, security, and compliance requirements.
Backend Services, Integration & Security
. Own the architecture and development of high-performance FastAPI services, including streaming responses, asynchronous processing, custom middleware, rate limiting, caching, and authentication and authorization strategies.
. Design resilient integration patterns for APIs and legacy platforms, including retries, idempotency, timeouts, circuit breakers, fallbacks, and auditability.
. Ensure solutions follow security, privacy, responsible AI, secrets management, input/output validation, and regulated data handling best practices.
Observability, Reliability & Optimization
. Implement comprehensive observability using LangSmith, distributed tracing, monitoring, logging, evaluation, error handling, and performance tuning for AI agent workloads.
. Define service level objectives and quality measures for latency, answer quality, tool accuracy, task completion rate, reliability, and cost.
. Identify and prioritize technical improvements in agent performance, latency, tool accuracy, scalability, and overall system architecture.
Technical Leadership & Collaboration
. Collaborate cross functionally with frontend, DevOps, product, security, data, and enterprise architecture teams to deliver end to end AI capabilities with high reliability.
. Lead architecture reviews, mentor developers, communicate design trade offs, and establish implementation guidance for distributed delivery teams.
. Evaluate emerging AI technologies pragmatically and recommend adoption based on measurable business value and enterprise readiness.
Required qualifications to be successful in this role
. 12+ years of professional software engineering and solution architecture experience, including hands on delivery of enterprise applications and platforms.
. 5+ years of AI/ML experience with recent hands-on delivery of production Generative AI and agentic AI solutions.
. Expert level Python skills and strong experience designing scalable, secure, production grade backend services using FastAPI.
. Hands on experience with LangGraph or a comparable framework for multi agent orchestration, including routing, handoffs, shared state, memory, tool use, recovery, and conversational continuity.
. Experience designing and implementing MCP servers and governing tools, resources, prompts, schemas, permissions, and lifecycle management within an agent ecosystem.
. Deep understanding of LLMs, prompt engineering, RAG, embeddings, vector search, memory patterns, function/tool calling, model evaluation, and guardrails.
. Proven experience designing AI powered chatbots, insights engines, or workflow automation solutions that integrate with enterprise APIs, data sources, and legacy systems.
. Strong knowledge of API and distributed system patterns, including streaming, asynchronous processing, middleware, rate limiting, authentication, authorization, resilience, and observability.
. Experience implementing LangSmith or equivalent AI observability, tracing, evaluation, debugging, and performance monitoring capabilities.
. Strong understanding of cloud native architecture, containers, CI/CD, security, privacy, responsible AI, and operational support for regulated enterprise workloads.
. Demonstrated ability to lead architecture decisions, mentor engineering teams, facilitate design reviews, and communicate complex tradeoffs to technical and business stakeholders.
Expectations
. Own solution architecture end to end, from discovery and design through implementation, production readiness, and adoption.
. Balance innovation with reliability, security, cost, maintainability, and measurable business outcomes.
. Drive architectural simplification, reuse, and consistent engineering practices across AI implementations.
. Operate as a hands-on technical leader who can validate critical designs and implementation patterns through working code and prototypes.
. Build trusted partnerships across product, engineering, DevOps, security, data, and enterprise architecture teams.
Mojhan Krishna Yarramsetti
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