Senior AI Solutions Developer

Summary:

We are seeking a highly motivated and experienced Sr. AI Solutions Developer who operates with high autonomy, owns pre-implementation decisions, enforces OWASP and cost-first discipline, and elevates the team's technical baseline through mentorship and shared tooling. As a Sr. AI Solutions Developer Your primary outputs are production-grade AI systems, architectural decisions the team can build on, and a documented, reusable knowledge base. You are accountable for enforcing the full OWASP LLM Top 10 mitigation stack and STRIDE threat modeling for secure coding.

Job Details:

Work from home

Monday to Friday | 9 AM to 6 PM

Responsibilities:

Lead AI Solutions pre-implementation review: model selection, cost benchmarking, hosting strategy, prototype trade-offs documented before any build begins

Architect and implement LLM pipelines: prompt engineering, RAG, structured output, tool use, multi-agent flows

Design and build REST APIs and data pipelines connecting AI components to Knit and client systems

Own repo-level AI configuration, shared prompt libraries, agent configs

Conduct code reviews with written feedback; mentor Junior developers; set and document best practices

Review SNS/SQS message contracts and integration impact before any cross-service AI merge

Lead OWASP LLM Top 10 (2025) red-team testing on every project before production release

Ensure STRIDE threat model is complete for every new AI system, covering data poisoning, prompt injection, model extraction, and excessive-agency risks

Collaborate with cross-functional teams to gather requirements and propose AI-based solutions that address business needs and drive innovation.

Stay abreast of emerging AI technologies and industry trends to identify opportunities for enhancing the organization's AI capabilities.

Evaluate the effectiveness of AI solutions, continuously refining and optimizing them to ensure optimal performance.

Develop comprehensive documentation for AI solutions, including technical specifications for AI features, LLM APIs, ML Libraries, vector stores, etc.

Serve as an AI evangelist, promoting the understanding and adoption of AI technologies across the organization through presentations, workshops, and training sessions.

Provide technical support and troubleshooting for AI implementations, ensuring the prompt resolution of issues and minimal disruption to users.

Qualifications:

Bachelor’s degree in computer science, Engineering, or a related field. Advanced degrees are highly desirable.

4+ years professional software engineering/development, with 2+ years focused on production AI/ML or LLM integration

Deep Python fluency, i.e. FastAPI or equivalent backend frameworks for production AI services

Hands-on LLM API experience: Anthropic Claude, OpenAI GPT-4, or equivalent — including structured output, tool use, and agentic patterns

Solid RAG implementation: chunking strategies, vector stores (Pinecone, Weaviate, pgvector), embedding models, retrieval validation

Document intelligence: OCR pipelines, PDF extraction (PyMuPDF, pdfplumber, AWS Textract, Docling)

AWS services: Lambda, S3, Bedrock, SageMaker or equivalent cloud AI platform

OWASP LLM Top 10 (2025) compliance. Can identify, mitigate, and red-team test all 10 risks in production AI systems

OWASP ASVS Level 2 secure coding application to API design, authentication, and data handling

STRIDE threat modeling for AI systems covering data poisoning, prompt injection, model extraction, excessive agency

Model/API selection for choosing the right model tier for the task (cost-performance fit, not default-to-best)

Cost-per-request benchmarking with documented analysis extrapolated to 6–12 months at projected scale

Hosting strategy, e.g. serverless vs self-hosted decision with infrastructure cost trade-off

Prototype trade-off report, ex. 2–3 model options tested with documented accuracy, latency, and cost results

Strong knowledge of AI technologies, including machine learning, natural language processing, and computer vision.

Exceptional problem-solving and analytical skills, with a proven ability to design and implement innovative solutions.

Excellent communication and interpersonal skills, with the ability to effectively collaborate with diverse teams and convey complex technical concepts to non-technical stakeholders.

Nice to Have:

Multi-agent frameworks: LangGraph, CrewAI, AutoGen, or custom orchestration

ISO 42001 AI Management System controls

EU AI Act risk classification and technical documentation

Philippines DPA 2012 and GDPR Article 25 (privacy by design) applied to AI system architecture

Amazon Connect or contact center AI integration experience

MCP (Model Context Protocol) server development

SBOM/AIBOM generation using CycloneDX or SPDX