AI engineer

AI Engineer

Company:

devx labs

Location:

Jakarta, Indonesia

Workplace Type:

On-site

Job Type:

Full-time

Company Description

devx labs is an AI consulting company that helps enterprises reimagine their business operations around advanced AI capabilities to deliver transformative customer experiences. The team combines the fresh perspective of AI-native talent with the practical expertise of experienced practitioners to drive meaningful change in the AI-first era. AI is treated as a core design principle, shaping the firm’s organizational structure, knowledge flows, and client engagement models. This approach enables devx labs to build solutions where AI is embedded into the fabric of business processes rather than used for incremental improvements.

Role Description

As an AI Engineer at devx labs, you will design, build, and deploy AI-native solutions that address complex business problems for enterprise clients in Jakarta, Indonesia. You will work on-site in a full-time capacity, collaborating closely with consultants, solutions architects, and software engineers to develop AI models, integrate them into production systems, and optimize their performance.

Day-to-day responsibilities include building and validating agentic capabilities on Gemini Enterprise, participating in system design discussions, writing clean and maintainable code, and helping refine AI workflows and tools used across client engagements. The role involves continuous learning, experimentation with modern AI frameworks, and sharing best practices with the broader team.

Responsibilities

Build AI-Native Solutions (65%)

• Engineer for impact:

  Build and validate agentic capabilities on Gemini Enterprise for a regulated banking client — spanning platform-level integration work (Microsoft identity and connector authorization inside a custom UI, human-in-the-loop approval flows for agent-initiated actions, private VPC-based agent connectivity) and business-facing agents (text-to-SQL reporting, anomaly detection for fraud/exception monitoring, sentiment and pattern analytics across complaint and feedback data).

• Architect with scale in mind:

  Create solutions that thrive in enterprise environments with thousands of users and millions of transactions.

• Ship velocity:

  Leverage AI-assisted development to deliver features 2–3x faster than traditional methods while maintaining code quality and reliability.

• Own technical decisions:

  Choose the right AI stack, model, framework, and architecture — balancing performance, cost, and maintainability.

• Solve across practices:

  Build customer service agents, inventory intelligence systems, or enterprise integration platforms depending on client needs.

Drive Client Success (25%)

• Translate business to technical:

  Participate in discovery workshops, understand client pain points, and architect AI solutions that deliver measurable outcomes.

• Demo with confidence:

  Present technical solutions to client stakeholders, explain architectural decisions, and build trust through technical credibility.

• Iterate with feedback:

  Work embedded with client teams, gather real-world usage insights, and continuously optimize solutions for adoption and impact.

• Bridge consulting and engineering:

  Collaborate with Solutions Consultants and Practice Leaders to turn strategy into executable technical roadmaps.

Mentor & Multiply (10%)

• Elevate the team:

  Guide junior engineers on AI-native development practices, code reviews, architectural thinking, and problem-solving approaches.

• Share knowledge:

  Document patterns, build reusable accelerators, and create technical playbooks that make the entire team more effective.

Qualifications

The Builder Mindset

• First-principles engineer:

  Question assumptions, explore alternatives, and design elegant solutions from the ground up.

• Outcome obsessed:

  Measure success in client impact, not lines of code.

• AI-native practitioner:

  Daily proficiency with AI tools (Claude Code, Cursor, GitHub Copilot).

Technical Superpowers & Experience

• Agentic AI Engineering:

  Hands-on production experience building and deploying agentic systems (multi-agent workflows, tool use / function calling, agent orchestration, evaluation, observability, guardrails, latency and cost management).

• AI/ML Depth & Foundations:

  Experience with LLMs, embeddings, vector databases, RAG architectures, neural networks, pattern recognition, and NLP tasks (text classification, information extraction, text-to-SQL).

• Partner-Platform Experience:

  Strong experience shipping on Gemini Enterprise / Vertex AI and AWS Bedrock.

• Full-Stack & Cloud-Native:

  Python development expertise alongside TypeScript/Node.js, React/Next.js, and GCP deployments (Cloud Run, Vertex AI, Cloud Functions).

• Background:

  Bachelor’s or Master’s degree in Computer Science, AI, Data Science, or a related technical field, with 2–6 years of software engineering experience (minimum 1 year focused on production AI/ML applications).

• On-Site Collaboration:

  Ability to work collaboratively on-site in Jakarta, Indonesia, communicate complex technical concepts to non-technical stakeholders, and engage with cross-functional teams.