AI Architect / Lead Data Engineer

Yondu, Inc.; Taguig; In loco; PHP 1.500.000 - 2.300.000; Tempo pieno

Vantaggi offerti da questo lavoro

Health insurance

Descrizione del lavoro

Yondu, Inc. is seeking a Senior AI Engineer to serve as the primary technical authority for client-facing AI engineering and deployment.

You will design, build, and deploy production-grade AI, Generative AI, MLOps, and agentic solutions for enterprise clients, bridging business needs with scalable technical architectures. You will lead end-to-end delivery from PoC to production, act as a trusted technical advisor to client teams, and set engineering standards across delivery projects in a

Competenze

• Proven experience architecting and delivering production AI systems.
• Extensive hands-on work with end-to-end AI/ML deployments.
• Strong client engagement and technical presales capabilities.
• Expertise in MLOps/LLMOps pipelines and cloud deployment.

Mansioni

• Architect and lead production-grade AI applications for clients.
• Define architectures, data models, and cloud patterns for deployment.
• Lead hands-on development, deployment, and monitoring of AI solutions.
• Deliver technical documentation and runbooks for client handover.
• Conduct technical discovery, PoCs, and MVPs with clients.

Conoscenze

Architectural design

Client facing

MLOps

Python

Cloud platforms

AI systems

Model evaluation

Prompt engineering

Strumenti

Docker

Kubernetes

LangChain

LlamaIndex

AutoGen

Descrizione del lavoro

General Responsibilities:

• 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 across delivery projects.

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 requirements.
• 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.

Technical Presales & Commercial Support

• 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.
• Soft Skills
  • 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 outcomes.