Founding AI Engineer (Agentic AI)

2y relevant experience

Under Review

For hiring agencies & HR teams

EU engineers, ready to place with your US clients

Pre-screened on AI. Remote B2B contracts. View 5 full profiles free — AI score, skills report, interview questions included.

Executive Summary

The candidate is a 2-year AI/ML engineer with genuine hands-on experience building production Agentic AI systems, RAG pipelines, and enterprise AI applications in the Google Cloud ecosystem. Their experience is relevant but their technical toolstack diverges materially from what this role demands — they have no demonstrated experience with LangGraph, LangFuse, CrewAI, LlamaIndex, OpenAI, or Anthropic APIs, which are core to AlpacaRelay's likely stack. The absence of any public code portfolio, GitHub profile, or open-source contributions is a notable weakness for a Founding AI Engineer position. Their India-based location and relatively early career stage (at the minimum experience threshold) also raise questions about fit for the seniority, timezone demands, and ownership expectations of a founding engineering role. They are a borderline candidate worth a screening conversation to assess adaptability, depth of AI knowledge, and remote work feasibility, but would need to demonstrate strong technical breadth and rapid learning ability to advance.

Top Strengths

✓Real production experience building and deploying Agentic AI and multi-agent systems
✓Proven ability to deliver quantified business impact through AI engineering
✓Strong GCP and cloud-native deployment experience (Cloud Run, Vertex AI, Cloud Composer)
✓Experience with RAG architectures and LLM security frameworks
✓MCA from a reputable Indian institution with a relevant computer science foundation

Key Concerns

!No familiarity with the core LLM toolstack required (LangGraph, LangFuse, CrewAI, LlamaIndex, OpenAI/Anthropic APIs)
!No publicly verifiable code or open-source contributions — critical gap for a founding engineer role at a startup

Culture Fit

55%

Growth Potential

Moderate

Salary Estimate

$30,000–$60,000 USD annually (India-based market rate); may be significantly below the $80K–$120K range listed, which could indicate a mismatch in expectations or role seniority

Assessment Reasoning

The candidate is classified as BORDERLINE rather than FIT or NOT_FIT due to a mixed profile: they meets the minimum experience threshold (2+ years) and has genuine Agentic AI and production AI deployment experience that is directionally aligned with the role. However, they are missing a significant portion of the required technical stack — specifically LangGraph, LangFuse, CrewAI, LlamaIndex, OpenAI APIs, Anthropic APIs, SciPy, Kubernetes, and MCP server experience — which are explicitly listed as required or preferred skills. The complete absence of a code portfolio, GitHub profile, or open-source work is a material gap for a founding engineer role where technical credibility and initiative are paramount. Additionally, their India-based location introduces practical considerations around timezone alignment and B2B contract eligibility for a Boston-based startup. Their overall score of 52 places them in the borderline range, warranting an HR screening call to assess technical adaptability, depth of AI systems knowledge, and logistics feasibility before making a final recommendation.

Interview Focus Areas

Depth of hands-on Agentic AI architecture experience — probe specifics of agent orchestration, tool calling, and memory managementAbility and willingness to rapidly adopt LangGraph, LangSmith, OpenAI/Anthropic APIs from GCP-native backgroundExperience and comfort working in early-stage, fast-moving startup environments with high ownershipUnderstanding of prompt engineering, evaluation frameworks, and AI observability practicesRemote work logistics, availability, and work authorization for a US B2B contract role

Code Review

FairMid Level

No code example or GitHub profile was provided by the candidate, which is a significant gap for a Founding AI Engineer role where hands-on technical evaluation is critical. The resume descriptions suggest awareness of solid engineering practices, but without code artifacts, this dimension cannot be properly assessed. This absence itself is a mild red flag for a senior founding engineering candidate.

+Resume describes structured, production-grade system design (multi-layer security, audit logging, metadata-driven models)
+Demonstrates awareness of scalability and reliability in system architecture

-No code sample was provided, making direct code quality assessment impossible
-No GitHub profile submitted, limiting visibility into coding style, open-source engagement, or project breadth
-Cannot verify hands-on coding ability or engineering craftsmanship from resume alone

Experience Overview

2y total · 2y relevant

The candidate is a 2-year software engineer with meaningful Agentic AI and GenAI experience, primarily within the Google Cloud ecosystem. Their work on supply chain AI platforms and multi-agent systems demonstrates real production AI experience, though their toolstack diverges significantly from the LangGraph/LangSmith/OpenAI/Anthropic stack this role requires. They have a strong foundation but would need to rapidly upskill on the specific frameworks and APIs central to this position.

Matching Skills

PythonNumPyRAG (Retrieval-Augmented Generation)Multi-Agent SystemsPrompt EngineeringDockerCI/CDREST APIsCloud Infrastructure (GCP)AI Agent DevelopmentLLM IntegrationFastAPIPostgreSQL (via Cloud SQL)

Skills to Verify

LangGraphLangSmithLangFuseCrewAILlamaIndexSciPyOpenAI APIsAnthropic APIsVector Databases (explicit)MCP Servers and Tool IntegrationsKubernetesAWSGitHub ActionsLangSmith or equivalent observability tooling

Candidate information is anonymized. Personal details are hidden for fair evaluation.