AI Developer (Metro Manila) at Lewis Global Communications

Manila, Metro Manila, Philippines -

Full Time

Start Date

Immediate

Expiry Date

19 Aug, 26

Salary

0.0

Posted On

21 May, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, PyTorch, TensorFlow, Keras, Hugging Face, LangChain, LlamaIndex, Generative AI, MLOps, AWS SageMaker, Google Vertex AI, Azure AI, SQL, NoSQL, Vector Databases, Prompt Engineering

Industry

Marketing Services

Description

About TEAM LEWIS TEAM LEWIS is a global marketing agency that has moved from start-up to multi-national in a little over two decades. Its success is due to a combination of factors: it’s ability to read the future and prepare for it; leveraging cutting edge AI; strategic thinkers delivering award winning campaigns; expanding client relationships into new markets or services; and above all, independence from outside investors. The practice, its people and its campaigns have won many awards, including Cannes Lions, PRovoke Media, ICCO, European Excellence, PRCA Digital, Digital Impact, Global Digital Excellence. At the heart of our community is the TEAM LEWIS Foundation (TLF), a non-profit charitable entity. Since its launch in 2021, it has funded over 2,600 causes. It is one of the largest employee-directed philanthropies in the world. The TLF promotes a wide variety of social, cultural, and environmental causes that benefit society. It aims to strengthen the bonds between the creative industries, businesses, government, and non-profits. Community charities are championed by employees who, in addition to the financial donation, volunteer their time and skills to maximise grant effectiveness. You can read more here about TEAM LEWIS Foundation. Purpose of the role You are a forward-thinking, hands-on AI Developer who turns business problems into production-ready AI capabilities, such as building, fine-tuning, and deploying machine learning and Generative AI solutions (including LLMs, RAG, and multimodal models) that are scalable, secure, and measurable. You will own the end-to-end lifecycle from data preparation and experimentation through to engineering-quality delivery (APIs, integrations, cloud deployment on AWS/GCP/Azure, and strong MLOps practices like CI/CD, version control, monitoring, and iteration), while applying sound judgement around model performance, bias, privacy, and responsible AI, and continuously staying current with fast-moving tools and research. Responsibilities Development responsibilities Build and maintain AI/ML solutions using Python as the primary language, with Java/C++ where high-performance services are required and R for statistical analysis/prototyping when needed Develop, train, evaluate, and optimize models using PyTorch, TensorFlow, and Keras Implement classical ML pipelines and experimentation using scikit-learn (feature engineering, model selection, tuning, validation, and performance measurement) Build NLP/LLM capabilities using Hugging Face (model selection, fine-tuning, inference optimization, and evaluation) Design and deliver RAG and LLM orchestration workflows using LangChain and/or LlamaIndex, including prompt chaining, tool use, routing, and guardrails Execute prompt engineering and fine-tuning strategies to improve quality, reliability, and task performance across use cases Develop and integrate multimodal AI solutions spanning text, image, audio, and video (as applicable), including data preparation and evaluation Apply strong linear algebra, calculus, probability, and statistics to diagnose model behaviour, improve training stability, and interpret results under uncertainty Design and manage data storage and retrieval patterns across SQL and NoSQL systems, ensuring clean, accessible, well-governed datasets Implement semantic search and “AI memory” patterns using vector databases (e.g., Pinecone, Milvus), including embedding strategies, indexing, and retrieval tuning. Deploy, scale, and operate AI workloads on AWS SageMaker, Google Vertex AI, and/or Azure AI, including endpoint management and cost/performance trade-offs Establish and follow MLOps best practices: Git-based version control, reproducible experiments, automated testing, CI/CD, model registry, and monitoring for drift/performance Debug and improve “black box” behaviour through systematic experimentation, root-cause analysis, and clear documentation of findings and decisions. Ensure solutions meet Responsible AI standards: privacy-by-design, secure data handling, bias detection/mitigation, and compliance with relevant legal/ethical requirements Collaborate with stakeholders to translate business needs into technical requirements, success metrics, and delivery plans Stay current with new research and tooling (e.g., arXiv, model releases, framework updates) and proactively recommend improvements to the team’s AI approach. Agency / client communication: Maintains regular day-to-day contact with key internal stakeholders to manage their expectations Works to ensure clear communication with internal and external contacts through the most appropriate method Confident communication, both written and verbal Team management and leadership: Encourages and inspires excellence in both the development and design teams Motivates colleagues to work together in the most efficient manner including Project Management and Client Services Actively challenges the status quo looking for ways to improve solutions recommended across teams Consistently acknowledges and appreciates each team member's contribution Records and shares lessons learned with team members to facilitate continuous improvement Client relationships and new business opportunities: Demonstrates an understanding of clients’ businesses and the context of their projects Identifies additional opportunities as they relate to a specific project or client Continually seeks opportunities to improve delivery against customer requirements Be mindful of wider agency and client objectives and work with all areas of the business to deliver against them About you Essential Skillset Technical & Programming Skills Strong knowledge in Python, Java, C++, and R Mastery of PyTorch, TensorFlow, and Keras for model development AI Libraries: Scikit-learn (general ML), Hugging Face (NLP/LLMs), LangChain/LlamaIndex (for RAG and LLM orchestration). Generative AI Tech: Proficiency in fine-tuning, prompt engineering, and working with multimodal models (Text, Image, Audio, Video). Workflow of machine learning for effective data processing outline diagram Knowledge in agentic workflow tools, n8n, Zapier, make.com or similar Data Engineering & Infrastructure Data Management: SQL and NoSQL databases, plus experience with Vector Databases (like Pinecone or Milvus) for AI memory. Cloud Platforms: Deployment experience on AWS SageMaker, Google Vertex AI, or Azure AI. MLOps: Knowledge of CI/CD for machine learning, version control (Git), and mode Confident communication, both written and verbal – occasional client meetings may be required A proactive approach and an ability to work independently Must have strong communication skills, written and verbal A well-rounded understanding of modern web technologies Open to shifting hours Experience of delivering technically sophisticated/complex creative solutions – demonstrable ability to talk the talk with clients, suppliers, and business stakeholders This job description is not intended to be an exhaustive list of the responsibilities for this role. Other responsibilities may be added from time to time. TEAM LEWIS is an Equal Opportunity Employer. We are committed to creating and fostering an environment focused on equality, empowerment, and respect. We strive to create an inclusive workplace that supports and celebrates our diversity. We continue to invest in our efforts to ensure that TEAM LEWIS is a place where everyone can thrive.

Responsibilities

Develop and deploy production-ready AI capabilities, including LLMs, RAG, and multimodal models, using MLOps best practices. Collaborate with stakeholders to translate business needs into technical requirements and ensure solutions meet Responsible AI standards.