Founding AI Engineer (Agentic AI)
5y relevant experience
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 technically capable Senior AI/ML Engineer with nearly 7 years of experience and a strong foundation in the core technologies central to this Founding AI Engineer role — particularly LangGraph, RAG architectures, LLM integration, agentic workflows, and full-stack Python development. Their current role at CodeFulcrum demonstrates direct production experience with modern AI tooling, and their project portfolio spans multimodal AI domains relevant to AlpacaRelay's content creation focus. Key gaps include several explicitly required tools (LangSmith, LangFuse, CrewAI, LlamaIndex, Anthropic APIs, Kubernetes, MCP Servers) and the absence of a submitted code sample or GitHub profile, which limits independent validation of engineering quality. The role's 'Founding Engineer' expectations — including technical leadership, architectural ownership, and startup-speed iteration — make this a borderline-to-fit candidate who warrants a structured technical interview to confirm depth before advancing. Timezone and B2B contractor considerations (Pakistan-based, Boston startup) should also be addressed early in the process.
Top Strengths
- ✓~7 years of progressive AI/ML engineering experience with clear career growth from Python developer to Senior AI/ML Engineer
- ✓Direct hands-on experience with LangGraph, RAG pipelines, LangChain, agentic AI workflows, and LLM orchestration in production — core to this role
- ✓Strong full-stack AI delivery capability spanning model development, API integration, frontend connectivity, and cloud deployment on AWS
- ✓Demonstrated multimodal AI experience (text, image, computer vision) aligning with AlpacaRelay's content creation product focus
- ✓Cross-industry experience (finance, healthcare, technology) indicating adaptability and ability to apply AI to diverse real-world problem domains
Key Concerns
- !Multiple explicitly required tools are missing from the profile (LangSmith, LangFuse, CrewAI, LlamaIndex, Anthropic APIs, MCP Servers, Kubernetes), reducing confidence in immediate production readiness for the full stack
- !No code sample or GitHub profile provided, making it impossible to independently verify engineering quality, coding standards, or technical depth — critical for a Founding Engineer role
Culture Fit
Growth Potential
High
Salary Estimate
$60,000 - $90,000 USD annually (Pakistan-based remote contractor; B2B structure may affect net expectations relative to the $80K-$120K range posted)
Assessment Reasoning
The candidate is assessed as FIT with moderate confidence (68%) based on the following reasoning: They meets the core minimum requirements of the role — 7 years of experience (well above the 2+ year minimum), proven Python expertise, demonstrated LLM/agentic AI engineering in production (LangGraph, RAG, LangChain, OpenAI APIs), and cross-stack delivery capability including cloud deployment. Their experience at CodeFulcrum is directly relevant to the responsibilities listed. However, the FIT decision carries important caveats: (1) Multiple explicitly required skills are absent from their profile — LangSmith, LangFuse, CrewAI, LlamaIndex, Anthropic APIs, MCP Servers, and Kubernetes — reducing confidence in full-stack readiness; (2) No code sample or GitHub profile was provided, which is a significant red flag for a Founding Engineer role where code quality and independent engineering judgment are paramount; (3) Their LinkedIn profile could not be assessed. The candidate clears the 70-point threshold primarily on the strength of their resume experience and skill alignment with core LLM/agentic AI requirements. A rigorous technical interview with a coding assessment is strongly recommended before making a final offer, with particular focus on the missing tool competencies and architectural decision-making ability.
Interview Focus Areas
Code Review
No code sample was provided, which significantly limits the ability to assess technical depth, coding standards, and engineering quality. The project descriptions in the resume suggest a reasonable level of system design thinking, particularly around RAG pipelines and computer vision tasks. A technical assessment or take-home challenge would be essential to validate actual coding ability before making a final hiring decision.
- +Project portfolio demonstrates diverse technical scope — spanning ML pipelines, RAG workflows, computer vision, and LLM automation — suggesting breadth of practical engineering ability
- +Project descriptions indicate familiarity with end-to-end system design (data ingestion, embedding, vector storage, LLM inference, output formatting) in the RAG Earnings Report project
- -No actual code was submitted for review, making it impossible to assess code quality, architecture patterns, testing practices, documentation standards, or engineering rigor directly
- -No GitHub profile was provided, removing the most common proxy for evaluating open-source contributions, coding style, and engineering maturity
Experience Overview
7y total · 5y relevantThe candidate presents a strong and well-rounded AI/ML engineering profile with nearly 7 years of experience, demonstrating solid alignment with core LLM, agentic AI, and RAG-related requirements. Their current senior role at CodeFulcrum directly involves LangGraph, RAG pipelines, prompt engineering, and production AI deployment — all central to this position. However, notable gaps exist in several explicitly required tools (LangSmith, LangFuse, CrewAI, LlamaIndex, Anthropic APIs, Kubernetes, MCP Servers), and the absence of a GitHub profile limits independent verification of code quality and engineering depth.
Matching Skills
Skills to Verify