AI Architect / Lead Data Engineer

Yondu, Inc.; Taguig; On-site; PHP 1,800,000 - 2,800,000; Full time

Job summary

Yondu, Inc. in Taguig is seeking a Senior AI Engineer to lead client AI architecture, design production-grade AI systems, and drive end-to-end delivery from PoCs to scalable deployments.

The role requires hands-on MLOps, LLM orchestration, and strong cloud computing experience across AWS/Azure/GCP, with a focus on secure, scalable enterprise solutions for clients.

Qualifications

• 10–12 years of software engineering, system architecture, data engineering, and ML/AI development.

Responsibilities

• Architect, design, and lead the technical delivery of production-grade AI applications and Generative AI solutions for enterprise clients.

Skills

Client-facing delivery

AI architecture

MLOps pipelines

LLMOps

Python

Cloud platforms

LangChain

RAG pipelines

LLM orchestration

Tools

LangChain

LlamaIndex

AutoGen

Docker

Kubernetes

AWS

Azure

GCP

Job description

• Serves as the primary technical authority and lead architect for client-facing Artificial


• Intelligence engineering and deployment. This role is responsible for designing, building, and deploying


• production-grade AI, Generative AI, MLOps, and agentic solutions directly for enterprise clients. The Senior AI Engineer


• bridges complex client business requirements with cutting-edge technical architecture, leading end-to-end technical


• delivery from initial proof-of-concept through to scalable, secure, and production-ready deployments. This role acts as a


• trusted technical advisor to client engineering and executive teams while setting technical standards for AI engineering

Duties and Responsibilities:

Client AI Architecture & Solution Engineering

• Architect, design, and lead the technical delivery of production-grade AI applications, Generative AI solutions, Retrieval-Augmented Generation (RAG) pipelines, and autonomous agentic systems tailored to client


• Define technical architectures, component interactions, API designs, data models, and cloud infrastructure patterns required to deploy robust AI solutions into client environments.


• Establish technical standards for model selection, latency optimization, context window management, and cost-effective inference in client production systems.


• Translate client business challenges and functional requirements into scalable technical solution architectures and engineering specifications.


• Ensure delivered client solutions seamlessly integrate with legacy enterprise architectures, cloud environments, and third-party systems.

Hands-On Technical Delivery, Deployment & MLOps

• Lead hands-on development, implementation, model integration, and refinement of AI solutions using state-of-the-art frameworks, libraries, and enterprise cloud platforms.


• Build and maintain robust MLOps and LLMOps pipelines for automated continuous integration, deployment, monitoring, evaluation, and lifecycle management of client models.


• Perform model fine-tuning, prompt engineering, vector database indexing, semantic search optimization, and orchestration for complex multi-agent workflows.


• Conduct rigorous testing, performance tuning, and benchmarking to meet client SLAs for latency, throughput, accuracy, and reliability.


• Package and transition completed technical deliverables to client teams with comprehensive technical documentation, handover sessions, and operational runbooks.

Technical Discovery & Feasibility Assessment

• Lead deep-dive technical discovery sessions and engineering workshops with client technology teams to evaluate technical feasibility, system constraints, and data readiness.


• Audit client data assets, APIs, and technical infrastructure to determine requirements for successful AI integration.


• Develop high-impact prototypes, proofs-of-concept (PoCs), and minimum viable products (MVPs) to rapidly validate complex client use cases prior to full delivery.


• Define concrete success metrics, accuracy benchmarks, and technical SLAs for client AI engagements.


• Partner with solution architects, delivery leads, and commercial teams to author technical solution designs, architectural diagrams, and implementation roadmaps for client proposals.


• Provide expert technical estimation (LOE, team composition, infrastructure costing) for complex client AI engineering projects.


• Conduct high-level technical presentations, solution demonstrations, and architecture reviews for client CTOs, lead architects, and engineering directors.

AI Governance, Security & Model Reliability

• Architect and implement robust enterprise guardrails, access controls, data privacy rules, and security frameworks within client AI deployments.


• Mitigate production risks including hallucination, prompt injection, data leakage, model drift, and bias through automated evaluation and observability frameworks.


• Ensure client deployments comply with relevant regulatory frameworks, legal guidelines, cloud security benchmarks, and responsible AI standards.

Technical Leadership & Engineering Quality

• Serve as the senior technical authority across client AI engagement teams, providing architectural guidance, code reviews, and technical problem-solving.


• Mentor mid-level and junior AI engineers on the project team, establishing best practices in clean code, AI system design, continuous integration, and production engineering.


• Contribute reusable codebases, technical patterns, and architectural blueprints to internal engineering repositories.

Job Qualifications:

• Relevant Experience: Relevant Experience - At least 10–12 years of relevant experience in software engineering, system architecture, data engineering, and machine learning/AI development.


• Minimum 4–5 years of dedicated experience designing, architecting, and deploying AI/ML and Generative AI solutions into enterprise production environments.


• Proven track record of direct client-facing technical engagement, consulting delivery, or systems integration experience delivering production AI software.


• Extensive hands‑on experience building end-to-end RAG systems, LLM orchestrations (e.g., LangChain, LlamaIndex, AutoGen), AI agents, and fine-tuned open-source/proprietary models.


• Demonstrated expertise establishing enterprise MLOps/LLMOps pipelines, CI/CD, model evaluation frameworks, and deployment on major cloud platforms (AWS, Azure, GCP).


• Experience participating in technical presales, authoring architecture diagrams, estimating engineering scope, and conducting client executive technical briefings.


• Knowledge - Advanced understanding of LLM architectures, transformer models, embeddings, fine-tuning techniques (LoRA/QLoRA), and prompt engineering.


• Deep understanding of enterprise RAG architectures, vector search algorithms, hybrid retrieval, and knowledge graph integrations.


• Strong knowledge of modern MLOps/LLMOps platforms, model observability, evaluation metrics, and guardrail integration.


• Expert knowledge of microservice architecture, RESTful API design, asynchronous processing, containerization (Docker, Kubernetes), and serverless patterns.


• Comprehensive understanding of cloud security, data privacy standards, enterprise authentication/authorization (OAuth, RBAC), and responsible AI mitigation techniques.


• Advanced proficiency in Python and modern AI/ML libraries, frameworks, and deployment tools.


• Exceptional technical architecture, system design, and hands‑on coding capabilities.


• Strong technical communication and presentation skills, with the proven ability to explain complex AI engineering concepts to both technical client leads and executive stakeholders.


• Ability to troubleshoot complex production bottlenecks, model failures, latency issues, and distributed system bugs.


• Strong client consulting skills with a focus on practical execution, quality, and measurable delivery.