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Job Description

Description

- Sydney Location / Hybrid Working
- Move beyond prototypes and help turn bold ideas into secure, scalable enterprise capability.

Are you ready to help shape how AI is engineered, governed and scaled?

Westpac is accelerating the future of banking through responsible, human-centred AI. We are looking for a senior, hands‑on AI Engineer to complement the Chief AI Innovation Office within Data, Digital & AI, a team shaping how advanced analytics, digital platforms and AI are engineered, governed and scaled across a large and complex enterprise.

This is an opportunity to bring your technical depth to high‑impact work, influence enterprise engineering standards and help teams move promising ideas into reliable production services.

Your role

You will design, build, test, harden and operate AI capabilities that are safe, reliable and ready for enterprise use. This is a practitioner role for someone who enjoys solving difficult engineering problems, making thoughtful trade‑offs and staying close to the code and production environment.

You will lead through technical credibility and influence. You will guide others, strengthen reusable engineering patterns and help ensure AI is delivered responsibly from concept through to production.

Key responsibilities

- Design, build and ship complex AI platforms, services and products that solve meaningful business problems at enterprise scale.
- Engineer robust LLM, retrieval‑augmented generation, agentic and orchestration solutions, with reliability, evaluation, observability and governance built in from the start.
- Take AI capabilities from experimentation into production, including testing, deployment, monitoring, incident response and continuous improvement.
- Lift engineering standards across prompt and context management, model evaluation, security, privacy, compliance and Responsible AI guardrails.
- Make practical trade‑offs across model quality, latency, cost, risk and customer impact in fast‑moving delivery environments.
- Partner with product, platform, data, risk, security and business teams to scale AI safely and sustainably.
- Review designs and code, mentor engineers and establish reusable patterns that accelerate safe production delivery.
- Lead technical investigations, root‑cause analysis and durable fixes for complex AI system issues.

Key Ingredients for Success

- Significant hands‑on AI engineering experience gained in a complex corporate, technology or enterprise environment.
- A strong record of delivering AI, machine learning or LLM‑enabled systems that have been deployed, governed, monitored and operated in production.
- Solid software engineering foundations across Python, APIs, cloud services, distributed systems, CI/CD, automated testing and modern delivery practices.
- Deep practical experience with LLMs, RAG patterns, embeddings, agents, evaluation frameworks, orchestration, model lifecycle management and monitoring.
- Experience embedding safety, privacy, compliance, explainability, reliability and operational risk controls into AI solutions.
- The ability to shape technical direction, raise engineering standards and bring stakeholders with you through ambiguity and change.
- Clear communication skills across engineering, product, delivery, risk and senior stakeholder audiences.

What Success will look like

- AI solutions progress beyond prototypes to become trusted, secure and production‑ready enterprise capabilities.
- Engineering patterns are clear, reusable and adopted across teams.
- AI risks are identified and designed out early through strong architecture, governance and operating discipline.
- Systems are observable, resilient and continuously improved using real usage, feedback and performance data.
- Engineers around you grow through practical mentoring, thoughtful design review and strong technical examples.

Does this sound like you?

We’re all about creating a supportive and inclusive community. We welcome everyone – no matter your age, gender, background, or abilities. We also provide added support to welcome our veterans, Indigenous Australians, and neurodiverse community.

If you need any adjustments during the recruitment process, you can find out more information and additional contact details by visiting the "People with Disability and/or needing Accessibility Requirements" page on our website.

We are an applied AI lab building end‑to‑end software agents.

We're the makers of Devin, the first AI software engineer.

Our team is extremely talent‑dense.



Among our founding team, we have world‑class competitive programmers, former founders, and leaders from companies at the cutting edge of AI including Scale AI, Palantir, Cursor, Waymo, Tesla, Lunchclub, Modal, Google DeepMind, and Nuro.

Building Devin is just the first step—our hardest challenges still lie ahead. If you’re excited to solve some of the world’s biggest problems and build AI that can reason on real‑world tasks, apply to join us.

About the Role

Applied AI Engineers are how Cognition operationalizes AI modernization at the enterprise level. You don't demo Devin - you deploy it. You embed with engineering teams, integrate agentic workflows into how they actually build and ship, and drive the kind of measurable productivity gains that make adoption irreversible.

As one of our first Applied AI Engineers, you'll also shape the function itself - turning individual customer engagements into repeatable playbooks, digital learning content, and partner‑driven enablement models that allow Cognition to reach hundreds of thousands of engineers worldwide.

This role is perfect for someone who has strong engineering fundamentals, enjoys working directly with customers, and finds deep satisfaction in driving product adoption and having users unlock their potential with Devin - while also thinking systematically about how to scale those learnings to the next 100 teams.

In this role, you will:

- Embed with enterprise engineering teams to drive deep, lasting adoption of Devin — owning outcomes, not just onboarding
- Architect and implement agentic workflows across engineering, QA, support, data, and product — identifying where AI creates the highest leverage and building toward it
- Lead interactive programmes for enterprise engineering teams (live workshops, pair programming sessions)
- Guide customers through installing, configuring, and optimizing Devin and its associated tools (DeepWiki, MCP integrations, etc.)
- Pair‑program on live production problems to demonstrate high‑value usage patterns and accelerate the team's applied AI fluency
- Quantify impact — tracking productivity metrics, surfacing ROI stories, and making the business case for expanding Devin's footprint within accounts
- Work with leadership to scale field learnings into structured playbooks and best practices that scale beyond individual engagements
- Create enablement materials, best practices, and shared playbooks based on customer learnings

Requirements for the Role

- Degree in a STEM field or equivalent hands‑on experience
- 3+ years as a software engineer, technical consultant, deployment strategist, forward deployed engineer, solutions engineer or similar roles with strong coding proficiency (Python, JavaScript/TypeScript, or similar)
- Proven ability to communicate complex technical topics to diverse audiences
- Proven track record of driving technical adoption and measurable impact inside engineering organisations
- Strong commercial instincts; you understand that successful engagements grow accounts, and you operate accordingly
- Excellent verbal and written communication skills
- Demonstrated ability to learn and adapt exceptionally fast

You might excel if you…

- You've led developer enablement, platform adoption, or internal AI modernization initiatives — and you can point to the before/after metrics
- Have deployed or integrated LLM or agent‑based systems in production settings
- Have previously founded or joined early‑stage startups where autonomy and execution speed were critical
- You're energized by seeing a team's velocity compound after working with them — and you engineer those outcomes deliberately

Our Commitment

Equal Prospect. Cognition is an equal opportunity employer. We do not discriminate on the basis of any protected characteristic under applicable law. We are committed to providing reasonable accommodations for candidates with disabilities throughout the hiring process — please let us know if you need any. Cognition also considers applicants with criminal histories consistent with the SF Fair Chance Ordinance and similar local laws.

Data Privacy and Arbitration: By submitting your application, you agree to our Global Applicant and Job Candidate Privacy Notice. For US applicants,



by submitting your application, you agree to the Applicant Arbitration Agreement.

About BJAK

The original mission of BJAK is we believe people deserve smarter ways to plan, save and grow their money. This is the origin of our name.

Started in 2019, we built the first mobile‑first, insurance platform, enabling insurance to be accessible online by millions in the region. Today, its the leading insurance platform in Southeast Asia.

Today, we are expanding ways to help people in the region — this includes spending, saving, investing, exchanging, travelling, and more. Our mission is help people get more from their money every day.

We have teams working around the world, with over 20 nationalities from our offices and remotely, who truly enjoys their work. We are looking for the most talented and driven people we can find. We are looking for people who work for their passion, not counting hours. Who loves building great next‑generation products, not status quo. Who cares about redefining how everyone around us can get the best financial applications, not for an exclusive few.

If you're this person, we'd love to talk to you.

The Role

We are looking for applied AI engineers to build AI‑native systems for BJAK's AI Finance Agent.

This is not a research‑only role. We need builders who can use AI to automate real workflows, improve products, reduce manual work and make financial services easier for users and teams.

What You Will Build

- Build AI agents, workflows, tools and automations that solve real customer, product and operations problems.
- Apply LLMs, retrieval, tool calling, evaluations, guardrails and automation to practical financial workflows.
- Create AI‑assisted experiences for onboarding, support, KYC, risk review, CRM, operations, document handling and product intelligence.
- Work with product and engineering teams to identify what is worth building, then build it quickly and improve it in production.
- Design around real constraints: accuracy, hallucination risk, latency, cost, security, compliance and user trust.
- Turn manual processes into reliable AI‑enabled systems that can scale.

What We Look For

- Hands‑on engineering ability, not just AI strategy or management experience.
- Experience building AI agents, LLM apps, RAG systems, automation workflows, copilots or AI‑enabled tools.
- Strong coding ability in Python, TypeScript, JavaScript or similar practical stack.
- Good judgement on what should be automated and what should not.
- Able to ship working systems, test quality and improve reliability over time.
- Fintech, banking, support automation, risk, KYC, fraud, CRM or operations automation experience is a strong plus.

This Role Is Not For

- People who only want research projects with no production ownership.
- Candidates who treat AI as demos instead of working systems.
- Engineers who do not care about evaluation, reliability or user impact.
- People who overclaim AI impact without evidence.
- Builders who cannot work with product, ops and business teams.

Location

This role is remote, but candidates must be based in Australia. We are hiring specifically for this market, so applicants should already be based in Australia.

Language

English is our main working language across global teams. Strong English communication is required.

Interview Process

Our process is designed to move fast:

- Online assessment or practical task
- Technical interview
- CEO / final round

For strong candidates, we aim to complete the process and make an offer within 1 week from the start of the interview process. Candidates who complete assessments quickly will be prioritized.

What you’ll be doing

- Building batch and streaming pipelines with Dataflow, Composer and Pub/Sub, landing in BigQuery
- Shaping BigQuery models so queries run fast and costs stay sensible
- Building the data foundations that machine learning and AI models are trained and run on
- Writing the Terraform, networking and security that keep the platform sound
- Owning pipelines in production: monitoring, fixing, and automating releases through CI/CD
- Working alongside data scientists, analysts and engineers to deliver what they need
- Keeping an eye on new GCP services worth bringing in

What you'll bring

- Advanced Terraform and enterprise‑grade GCP cloud engineering
- Confidence with VPCs, subnets, load balancing, firewalls and DNS
- Python or Bash, plus Git, Docker and Kubernetes
- DataOps habits: automated testing and data quality built in from the start

The package

- $140K to $180K base, plus super and bonus
- Hybrid, with three days a week on site
- Australian work rights required; background checks apply

📌 Ai Agent Engineer: Build Production-Grade Agents (Sydney)
🏢 Sierra
📍 Sydney