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Salary
≈ $23k – $49k per year (Estimated)
Location
Hybrid (Pune, India)
Seniority
Architect · 12+ years exp

Confirmed on the employer's own hiring board on Nov 2, 2026. First seen by Alion on Apr 1, 2026. Altimetrik scores D on the Alion truth index.

Overview
Company
Impact
Profile match
Altimetrik is a digital and AI engineering services company headquartered in Southfield, Michigan, that builds software, data, cloud and AI systems for large enterprises in banking, insurance, automotive, healthcare, retail and technology. Founded in 2012 by Raj Vattikuti, the founder of Covansys, it employs between 5,000 and 10,000 people, with major delivery centers in Chennai, Bengaluru, Pune and Hyderabad and further offices in Krakow, Mexico City, Montevideo, Singapore, Canada and the US. It hires data, AI and generative AI engineers, Java, .NET and full stack developers, DevSecOps, cybersecurity and Salesforce engineers, architects and engineering managers.

JD - AI Solution Architect (Fintech · Web, Mobile & Agentic)

Role

The AI Solution Architect turns business goals into secure, scalable AI/ML and agentic solutions: reference architecture, integration, and governance -with payments-aware risk discipline.

Experience: 12-15 years in software/solution architecture; strong recent exposure to GenAI, agents, and production integrations.

Responsibilities

Area

Expectations

AI & agentic design

End-to-end solutions: LLM + tools (e.g. MCP-style),RAG/memory where grounding helps, guardrails, durable workflows (state, HITL, idempotency for multi-step flows).

Strategy & stack

Prioritize high-value use cases; define cloud/hybrid targets (AWS and/orAzure as applicable),model-agnostic patterns, cost/latency trade-offs.

Integration

APIs and events for web, mobile, and agentic clients;goal-oriented machine-facing contracts alongside traditional channels; integration with CRM (e.g. Salesforce) and core platforms.

Security & access

AuthN/AuthZ for users and automated actors (agents, services): least privilege, audit, secrets,no LLM→database direct access -tools/APIs only.

Operations

Observability for quality, latency, cost, abuse; evaluation and improvement loops (not one-off launches).

Compliance & ethics

Secure development, data minimization, alignment withfintech/regulatory expectations for AI use.

Evolution

Monolith → modular/API-first roadmaps (strangler, bounded contexts) includingagent-ready surfaces.

Technical profile

  • AI/ML & agents: Practical depth with examples such as PyTorch / TensorFlow (classical ML), LangChain / LangGraph / LlamaIndex or equivalent (orchestration & RAG), MLflow or cloud ML ops where used.
  • Cloud: AWS (primary ask) and/or Azure; identity, networking, observability.
  • Data: Yes - required: pipelines (batch/stream), quality, and timely data for models/agents-not optional at architect level.

Integration: REST/events, API gateways; Salesforce and enterprise systems in target-state designs alongside agents.

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