Projects/Career_Next_Roles
Career — Next Roles & Domain Opportunities
Prepared after reviewing Projects/RBC_Work (RBC network engineering AI initiatives) and the GitHub profile of Igor Manassypov (Cisco Systems Engineer). Goal: map current experience — Staff/Senior/Lead Software Engineer building AI-driven network validation, certification, telemetry-assurance, and chatops systems inside a large bank — onto realistic next roles across domains and company types.
[!note] Reference See "Industry Reference: Igor Manassypov" section at the bottom of Projects/RBC_Work for the raw repo-by-repo comparison this document is built on.
1. What the RBC Work Actually Proves (Skill Extraction)
Strip away the RBC-specific names and the work at Projects/RBC_Work demonstrates five transferable competencies:
- Closed-loop automation with AI-in-the-middle — pre-check → automated change (Ansible) → post-check → AI-generated analysis/report. This is a general "safe autonomous change" pattern, not network-specific.
- Telemetry/observability platform building at scale — ingesting sflow/netflow/ipfix + enrichment (Extrahop/Corvil) into Elasticsearch, then building blast-radius/dependency visualization on top. This is an observability/APM/security-data-platform skill, transferable to any org with distributed infra.
- Conversational ops interface (ChatOps) over heterogeneous backends — MCP-based chatbot federating device inventory, packet capture, and multi-tool queries (Grafana MCP, Elasticsearch MCP, CLI MCP). This is agentic-AI-over-enterprise-systems, a hot cross-industry skill right now.
- Compliance-as-code / audit-ready automation — ServiceNow integration so every change is audit-ready, OSFI-ready compliance tooling, config-drift detection against approved change records. This is regulated-industry engineering — a differentiator most SWEs outside finance/healthcare/telco don't have.
- Program-scale delivery — shipping across 29 concurrent AI initiatives spanning architecture, database, storage, compliance, and hosting/compute domains, i.e. operating as a technical lead across a portfolio, not just one codebase.
These five map cleanly onto roles well beyond "network engineer," which is the point of this document.
2. Domain Map: Where This Experience Transfers
2.1 Within Banks / Large Regulated Enterprises (same employer type, different domain)
Banks split roughly into Technology & Operations (T&O), Capital Markets, Risk/Compliance, and Enterprise AI/Platform groups. Current work sits in T&O (network engineering + enterprise AI agenda). Adjacent moves:
- Capital Markets Technology (Staff/Lead SWE) — trading infra, market data platforms, low-latency systems. The telemetry/observability skill (Aegis: flow data → blast radius) is directly relevant to market-data latency monitoring and trade-surveillance data pipelines. The audit-ready/compliance-as-code experience (ServiceNow-integrated changes, OSFI readiness) maps to trade compliance and regulatory reporting systems.
- Enterprise AI Platform / AI Governance Engineering — banks are all building internal "AI factory" teams (see RBC's 29 AI initiatives, Athena 3.0, AI SDLC). The pattern-digitization, blueprint-review-agent, and spec-driven-development work is literally platform work for an AI enablement team — a natural lateral into leading such a team at another bank or fintech.
- Site Reliability / Production Engineering (Capital Markets or Payments) — self-healing network agents (#12), config-drift detection (#28), and PIV pre/post-change validation (#9) are SRE primitives. Payments and trading systems need exactly this: automated safe-change validation with strict audit trails.
- Risk & Compliance Engineering — the "Document and Standard Compliance Analyzer" (#27) and "Patch discovery agent" (#26) are risk-tech: AI-assisted regulatory gap analysis and CVE/patch tracking. Risk tech teams at banks pay well and value exactly this compliance-automation skill set.
2.2 Outside Banks — Adjacent Regulated/Infra-Heavy Industries
- Telecom / ISPs / Cloud Providers (Cisco, Arista, Equinix, Cloudflare, hyperscalers) — this is Igor Manassypov's world. Direct competitor skill set: network automation via Ansible/Terraform, MCP servers over network platform APIs (Catalyst Center, Meraki), telemetry assurance via Splunk/streaming MDT. A move here would be a lateral (same domain, vendor side instead of enterprise-consumer side) — likely titled Staff/Principal Network Automation Engineer or Solutions/Systems Engineer at a networking vendor.
- Observability/APM vendors (Datadog, Grafana Labs, New Relic, Elastic, Splunk itself, Extrahop) — the Aegis experience (building the exact kind of product these companies sell) is a strong story for a Staff Engineer on a product team, not just a customer of one. Selling point: "I built and operated what your product does, at bank scale, under audit constraints."
- DevOps/Infra tooling vendors (HashiCorp, Ansible/Red Hat, GitHub/GitLab platform teams) — template-as-code/GitOps for network config (pattern digitization, Catalyst Center template sync) is directly relevant to a Staff SWE on a Configuration-as-Code or Policy-as-Code product.
- Security/Compliance vendors (Vanta, Drata, OneTrust, Wiz) — the compliance-analyzer and config-drift-authorization agents are literally the product category these companies sell (automated compliance evidence + drift detection). Strong Staff/Lead pitch for a compliance-automation startup.
2.3 Outside Regulated Industries — General Tech / AI-Native Companies
- AI Agent Infrastructure companies (Anthropic, LangChain, agent-tooling startups) — the MCP toolbox, multi-agent orchestration (autogen-based config-drift agent, #28), and spec-driven AI SDLC work are current-frontier agent-engineering experience. Strong candidate for Staff/Founding Engineer on agent tooling or agent orchestration platforms — this is the single most "hot market" transferable skill in the portfolio right now.
- DevTools / internal-platform teams at any large tech company — the AI-assisted SDLC (requirements → architecture → code → compliance evidence, #6-7) is exactly what internal developer-productivity teams (Uber, Stripe, Airbnb platform orgs) are building. Pitch as Staff Engineer, Developer Platform / AI-Assisted Engineering.
- Vertical SaaS with heavy infra/compliance overlap (healthtech, insurtech, govtech) — same compliance-as-code and audit-trail skills apply; these industries have similar regulatory pressure to banking (HIPAA/SOC2/FedRAMP instead of OSFI) and are earlier in adopting AI automation, so a Staff/Lead hire with this exact background is unusually valuable there.
3. How Company Size/Type Changes the Pitch
| Company type | What to lead with | Likely title |
|---|---|---|
| Another large bank / regulated enterprise | Compliance-as-code + audit-ready automation + AI SDLC at scale | Staff/Lead SWE, Principal Engineer |
| Networking/observability vendor | Aegis (telemetry platform) + MCP chatops + Ansible automation depth | Staff/Principal Engineer, Solutions Architect |
| Compliance/security SaaS startup | Config-drift agent, patch-discovery agent, compliance analyzer | Founding/Staff Engineer |
| AI agent infra company or startup | MCP toolbox, multi-agent orchestration, spec-driven AI SDLC | Staff/Founding Engineer, AI Platform Lead |
| Generalist big tech (platform/devtools org) | AI-assisted SDLC, architecture-as-code, scale of delivery (29 initiatives) | Staff Software Engineer |
4. Gaps Worth Closing Before Pursuing Each Path
- For AI agent infra roles: build one public, portfolio-visible agent project (mirrors what Igor Manassypov does openly on GitHub — his repos function as a public portfolio/resume). Currently all of this work is locked inside RBC; there is no public artifact demonstrating it. Consider open-sourcing a stripped-down version of one pattern (e.g., a pre/post-check validation agent against a lab network, or a general-purpose config-drift detector) the way his CatalystCenter-BGP-EVPN-VXLAN and MCP server repos do.
- For observability-vendor product roles: deepen hands-on depth in one specific telemetry stack end-to-end (not just consuming Extrahop/Corvil output) — matches his campus-bgp-evpn-splunk-assurance model-driven-telemetry (MDT/YANG gRPC) approach, which is a level below where Aegis currently operates (data already enriched by vendor collectors).
- For capital-markets tech: no direct evidence yet of trading/market-data domain knowledge — the AWS SAA-C03 certification (unresolved) in progress helps with cloud infra credibility but not markets-domain credibility; consider a short capital-markets-technology primer if pursuing this path seriously.
- For risk/compliance vendor roles: the compliance-analyzer and patch-discovery agent work (#26-27) is strong but undocumented outside RBC internal systems — write one anonymized case-study-style note (similar in spirit to a blog post) describing the architecture without RBC-confidential specifics, for interview storytelling.
5. Suggested Near-Term Actions
- Pick one lane from Section 3 based on actual interest (regulated-enterprise-adjacent vs. vendor/product vs. AI-agent-infra) rather than trying to pursue all five in parallel.
- If leaning AI-agent-infra or observability-vendor: start a small public GitHub presence now, following the pattern Igor Manassypov uses — narrow, well-documented, single-purpose repos rather than one large monorepo.
- If leaning capital-markets or risk/compliance within banking: use the AWS SAA-C03 study track already underway as a springboard, and start collecting anonymized "what I built" narratives from the 29 AI initiatives for interview use, since none of that work is externally visible today.
- Revisit this document quarterly as Projects/RBC_Work initiatives evolve — several (#4 AI-architecture-as-code at 72%, #2 pattern digitization at 125/175) are close to completion and will strengthen specific pitches once done.