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Agentic AI Solutions Architect

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Onsite

Agentic AI Solutions Architect

Boundaryless

Agentic AI Solutions Architect

Skills

DockerPythonAIKubernetesn8nFastAPICompliance

2 months ago

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Important information


Contract type:

Permanent

Location:

Pune, Maharashtra, India

Work mode:

Onsite

Published on:

4 September 2026

What they need


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.

Pune (on-site/hybrid).

Experience Requirements

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 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.

  • 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, and 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.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

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