Agentic AI Solutions Architect (Pune)

Job Category: Agentic AI Architect

Job Type: Work from Office

Job Location: Pune

Role Description

• The Agentic AI Architect is a senior technical leader who can architect and implement production-grade agentic systems.
• This role blends platform architecture, hands-on Python/LLM engineering, and enterprise governance.
• Co-architect end-to-end agentic solutions: orchestration patterns, service boundaries, integration architecture, and non-functional requirements.
• Define engineering standards and best practices for safe, observable, testable agentic workflows (tool calling, structured outputs, approvals, traceability).
• Provide hands-on technical leadership: design reviews, reference implementations, complex debugging, and performance/scalability tuning.
• Ensure enterprise readiness: security-by-design, audit evidence, data controls, and operational stability across DEV/UAT/PROD.

Location

• Pune (in office/hybrid).

Experience Requirements & Qualifications

Core Experience

• 8+ years in software engineering / platform engineering / solution architecture, including building production backend systems.
• 2+ years designing or building AI/LLM-enabled systems (agentic workflows, orchestration, RAG, evaluation/monitoring).
• Proven experience operating in enterprise environments with security, compliance, and change-management expectations (financial services preferred).
• Strong capability in distributed systems design: service decomposition, API contracts, asynchronous execution, retries/idempotency, failure isolation, and resilience.
• Experience designing workflow/agent execution architectures: worker patterns, job queues, scheduling, state management, and execution traceability.
• Ability to translate ambiguous objectives into implementable architecture and delivery plans (tradeoffs, risks, phased rollouts).
• Advanced Python engineering skills: clean architecture, modularity, testability, performance profiling, packaging, and secure coding.
• Robust experience building API-first services (FastAPI or equivalent), including auth patterns (OAuth2/JWT/API keys), versioning, and backwards compatibility.
• Strong schema/data contract practice using typed models and validation (e.g., Pydantic-style patterns).
• Deep understanding of reliable LLM interaction patterns, tool/function calling, and protected tool execution boundaries.
• Experience in implementing and integrating MCP servers for agentic use cases.
• Multi-step planning/routing, guardrails, and human-in-the-loop approvals.
• Grounding and retrieval patterns (RAG), citation/evidence generation, and prompt/version management.
• Strong understanding of different agentic frameworks, strengths, weaknesses, and ability to suggest custom framework designs.
• Test harnesses, golden datasets, regression testing for prompts/agents.
• Safety testing, hallucination mitigation strategies, and cost/performance controls.
• Observability: application event tracking, traces, metrics, decision logs, prompt lineage, dashboards, prediction capabilities, run replay.

Platform / DevOps Awareness (Strong Preference)

Comfortable with on-prem/cloud infra:

• Collaborate with infra teams on on-prem / hybrid environments.
• Containers (Docker), Kubernetes/OpenShift fundamentals (deployments, secrets, ingress).
• Logging/monitoring patterns, production readiness, capacity planning.
• Secrets management, RBAC, audit logging, and environment separation.

Nice-to-Have

• Experience with agent orchestration frameworks (e.g., LangGraph-like patterns) and LLM observability tools (Langfuse-like capabilities).
• Experience working with open-source frameworks and customizing existing agent frameworks to support complex business requirements.
• Experience with enterprise-hosted LLMs, and designing vendor-agnostic model abstraction layers.
• Exposure to orchestration tools such as n8n (good to have), especially for trigger/action workflows and enterprise automation.

Main Tasks and Responsibilities

1) Co-Architect Enterprise Agentic Solutions

• Co-own the reference architecture for agentic solutions (components, interfaces, execution model, security controls).
• Define solution patterns for routing/planning agents, tool execution agents, retrieval/grounding, approvals, and escalation paths.
• Define multi-agent or multi-step workflows with deterministic control points.
• Define evidence capture, explainability notes, and audit-ready outputs.

2) Establish Engineering Standards & Best Practices

• Create and enforce standards for prompt/agent versioning, structured output contracts, and validation.
• Protected tool execution (permissions, allow-lists, constrained actions).
• Error handling, retries, idempotency, and compensating actions.
• Logging/tracing conventions and runbook expectations.
• Define definition of done for production-grade agentic workflows (security, tests, observability, documentation).

3) Lead Technical Delivery

• Lead design reviews and support .