Senior AI Forward Deployed Engineer - Cogentiq I2C

Role overview

Senior AI Forward Deployed Engineer - Cogentiq I2C at Fractal

Responsibilities

• You will still be in the codebase.

Required qualifications

• 6 to 10 years building and operating production systems, including at least two years in customer-facing delivery.
• A track record of taking at least one non-trivial AI system to production and keeping it running afterwards.
• Strong Python and system design depth.
• Depth across LLM and agentic architectures, retrieval, evaluation methodology, guardrails and observability.
• Cloud deployment experience at scale.
• Containers and orchestration.
• Enough finance vocabulary to hold a conversation about DSO, deductions, remittance advice, and cash application without a translator or the evident ability to get there within a quarter.
• Commercial judgement.
• You can recognise when a customer request has quietly become unpaid scope, and you raise it early.
• Willingness to travel and embed with customers.

Preferred qualifications and skills

• Azure is preferred given our stack.
• AI deployments in regulated industries.
• Enterprise ERP integration experience, particularly SAP S/4HANA or Dynamics 365 Finance and Operations.
• Experience mentoring or leading engineers.
• Pre-sales or solution architecture exposure.
• Startup or product-building experience where customer feedback shaped what got built.
• What success looks like in your first year
• Two accounts running in production against agreed KPIs.
• The Forward Deployed Engineers on your team are measurably more capable than when they joined it.
• At least two reusable assets you produced are in use by another deployment team.
• If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!
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Employment details

• Location: Bengaluru, Mumbai

Additional role details

• It's fun to work in a company where people truly BELIEVE in what they are doing!
• We're committed to bringing passion and customer focus to the business.
• Senior AI Forward Deployed Engineer - Cogentiq I2C (Invoice-to-Cash)
• Role summary
• You own the technical success of Cogentiq I2C at a portfolio of strategic accounts.
• You take a business problem that arrives stated in finance terms, reduce our days sales outstanding, stop writing off small-balance deductions, and turn it into a production AI system that the customer’s finance organisation actually uses.
• You lead a small deployment team.
• You make the architecture calls.
• You are the person the customer sponsor calls when something is wrong, and the person who tells them what we will not build.
• This is an engineering role.
• The difference from the first band is that you also carry the account, the design and the team.
• What you will own
• Account technical ownership
• Own the technical outcome for a portfolio of accounts, from discovery through production rollout and scale.
• Set and defend the deployment KPI contract: accuracy, straight-through processing rate, latency, cost per transaction, adoption and business impact.
• Hold the technical relationship with customer sponsors and IT leadership.
• Solution design and architecture
• Translate ambiguous goals into a solution design covering agent topology, retrieval strategy, human oversight boundaries, integration approach and data model.
• Make and document the build versus configure versus extend-the-product calls.
• Decline bespoke work that cannot be maintained across the customer base.
• Own the integration design against customer ERP, banking and document infrastructure.
• Delivery leadership
• Lead a deployment team of Forward Deployed Engineers and specialists.
• Scope the work, sequence it and unblock it.
• Own proof-of-value and pilot scoping, including effort estimates and success criteria that can be measured rather than asserted.
• Act as incident commander for production failures.
• Own the root cause analysis and the remediation plan.
• Advisory
• Explain to finance and IT leadership what the system can and cannot do and what residual risk remains after it is deployed.
• Guide customers on governance, human oversight design, model risk and responsible AI.
• Product and asset leadership
• Convert deployment learnings into reusable assets: reference architectures, connector templates, evaluation packs and deployment accelerators.
• Bring evidenced feedback to the product roadmap.
• Distinguish a single customer’s preference from a genuine product gap.