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 (on-site/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.
• Strong 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 safe 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 on 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 on:

• 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
• Multi-agent or multi-step workflows with deterministic control points
• 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
• Safe 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 implementation teams (developers, infra, analysts).
• Provide hands-on guidance for complex builds: reference implementations, difficult integrations, performance bottlenecks, and incident triage.
• Mentor Agentic Developers; uplift overall code quality and engineering maturity through reviews and coaching.

4) Enterprise Security, Governance, and Risk Controls

Partner with security and client governance stakeholders to implement:

• Secrets handling, RBAC, identity integration patterns, audit logging
• Data residency and data handling controls, redaction/masking where required
• Change/release controls and environment promotion practices
• Ensure designs are compliant with regulated client expectations (especially BFSI).

5) Observability, Evaluation, and Continuous Improvement

• Define evaluation strategy (offline + online): regression suites, acceptance metrics, and monitoring thresholds.
• Implement/guide observability practices: traces, metrics, cost/performance tracking, decision logs.
• Drive post-go-live optimization: reduce failure rates, improve determinism, control latency/cost, and improve maintainability.