Artificial Intelligence

• Lead the design and implementation of scalable, reliable systems
• Make critical architectural decisions with a bias for simplicity and maintainability
• Own the health and performance of core services
• Review code and enforce high standards through mentoring and pair programming
• Mentor and support engineers through code reviews, pairing, and design discussions
• Identify skills gaps and help grow technical competencies across the team
• Drive continuous improvement in team processes and engineering culture
• Be a multiplier: unblock, enable, and elevate others
• Collaborate with product managers to scope and deliver features on time
• Break down complex problems into executable chunks
• Track technical debt and prioritize long-term engineering investment
• Proactively raise risks and navigate ambiguity during delivery

AI/ML Lead

• Design, build and operationalize agentic AI solutions in production
• Lead technical discovery, scope agentic AI use cases
• Write production-grade code to deliver solutions
• Build and tune retrieval pipelines, prompt architecture, guardrails, agent orchestration
• Implement enterprise-safe agent tool execution
• Build evaluation suites that catch hallucinations, regressions
• Stay engaged through cutover and first production iteration cycle

Forward Deployed Engineer – Agentic AI

• Lead executive-level workshops with public sector leaders to identify mission-critical challenges and opportunities where Mistral’s AI can drive step-change improvements.
• Co-create AI adoption roadmaps with government and institutional customers, articulating the “art of the possible” while addressing regulatory, ethical, and operational constraints.
• Collaborate with Account Executives to develop business cases, quantify ROI, and align solutions with public sector objectives—such as digital transformation, citizen services, and operational efficiency.
• Architect end-to-end AI solutions, integrating Mistral’s models and platform into public sector workflows and technical infrastructure.
• Partner with the Applied AI team to design, prototype, and deploy AI solutions in production, ensuring scalability, compliance, and public value.
• Own the execution of pilot projects and proofs-of-value, demonstrating the potential of our technology and paving the way for full-scale deployment in government and institutional settings.
• Serve as a trusted advisor to public sector customers, guiding their AI strategy and ensuring they maximize the value of their investment in Mistral.
• Monitor key performance indicators (KPIs) tied to both business and societal outcomes, and communicate progress to executive sponsors and political stakeholders.
• Proactively identify expansion opportunities within public sector accounts, building on initial successes to drive long-term partnerships.
• Act as the bridge between public sector customers and Mistral’s internal teams, synthesizing feedback to influence product and research roadmaps.
• Develop reusable assets, best practices, and playbooks to scale go-to-market efforts and ensure consistent delivery excellence in the public sector.

AI Deployment Strategist – Defence

AI Deployment Strategist, Public Sector

• Review cases escalated by ROCA where confidence score falls below the auto-decision threshold, using AI-assisted signals within the unified case review UI (no multi-system switching)
• Make accurate pass / block / escalate decisions on a daily queue spanning KYC Audit, ATO investigation, Reset 2FA, POA Review, and Withdrawal anomaly case types etc.
• Escalate Key User cases (Priority ≥ 75) and high-risk patterns to Senior Agent / Team Lead with clear rationale documentation
• Maintain SLA compliance (<4h manual resolution time) while sustaining decision accuracy ≥ 98%
• Label every reviewed case as TP / TN / FP / FN with structured root-cause annotations — this data directly feeds ROCA's continuous learning pipeline
• Identify recurring FP/FN patterns and route them through the correct feedback path
• Actively contribute to improving Precision (>95%) and Recall (>99%) targets by surfacing systematic signal failures to Data and Model teams
• Participate in regular AI model calibration sessions: review misclassification batches, validate ground truth, challenge decision thresholds with data
• Maintain and update Confluence SOP pages for assigned case types; SOP coverage rate target ≥ 80% (directly impacts ROCA first-pass accuracy)
• Flag SOP coverage gaps when ROCA's reasoning chain reveals unhandled scenarios; draft SOP additions for Team Lead review
• Onboard and calibrate BPO agents on AI-assisted review workflows; monitor BPO quality against automated QA benchmarks

Risk Operations AI Specialist

• Accountable for business growth, company market share and revenue increases.
• Coordinates all company sales activities in the area-of-control.
• Sets quota and goals for organizations.
• Develops tactics to generate new sales.
• Provides input to team on overall sales strategy, cost optimization, and disciplined process management (pipeline review, asset management, demo sales achievement, etc.).
• Ensures optimum sales coverage through direct and partner sales resources and different routes to markets.
• Develops sales resources and management talent to ensure a pipeline of qualified sales talent to support future growth.
• Implementation of master pipeline management.
• Builds lasting, consultative relationships with customer accounts.
• Proactive change management.
• Coach and support sales teams and leadership in developing key and/or difficult account opportunities.
• Builds long-term growth opportunities using the Account Business Planning process.
• Develops the consultative, solution selling capability in their organization to develop compelling business cases to differentiate and highlight the value of the company's broad portfolio.
• Ensures resource deployment encompasses end-to-end selling support (solution partners, SA, ISR, resellers, etc).
• Demonstrates in depth knowledge of the vertical segment and provides external leadership to industry, community, press.
• Engages Global Business Unit Sales Teams (Specialists, Channel Partners, and Alliance Partners) to fully leverage all of the company's products and technology offerings.
• Creates and manages resource plan including staffing of country sales team to ensure pursuit and closure of opportunities, and perpetuation the existing installed base revenue.
• Balances short term with long term planning and resource investment.
• Demonstrates thought leadership by directing the customer's application of technology to new business problems.
• Creates a performance driven culture that ensures the company has the best IT sales force in the industry.

HPC AI Sales Leader

• Design and build AI agent workflows and LLM-powered systems for real business use cases
• Develop and integrate tools for agents, including APIs, internal services, databases, knowledge sources, and operational systems
• Improve agent quality, latency, reliability, safety, and cost through testing, evaluation, and iteration
• Build prompt structures, orchestration logic, memory or retrieval patterns, and human handoff flows where needed
• Collaborate with product, business, and operations teams to translate ambiguous business problems into practical AI solutions
• Contribute to backend services, internal platforms, and lightweight interfaces to support end-to-end delivery
• Help define reusable engineering patterns, guardrails, and evaluation approaches for production AI agents

Talent Program – AI Agent Engineer

• Designing and shipping production-ready AI agent systems that power workflows inside trysecretsauce.ai
• Building and refining agent loops, including planning, tool use, reflection, and retry strategies
• Owning prompt engineering and context engineering as core disciplines, including structured prompts, versioning, template libraries, and few-shot strategies
• Architecting retrieval systems using RAG approaches, including hybrid search, reranking, chunking, and document pipelines
• Designing memory systems, including short-term task memory and long-term user or agent memory across sessions
• Building and maintaining evaluation harnesses, including benchmarks, A/B testing, golden datasets, and regression suites
• Optimising cost and latency using techniques such as prompt caching, KV reuse, streaming, model routing, and response caching
• Building agent infrastructure, including sandboxes, tool wrappers, MCP servers, permission layers, and observability
• Taking features from prototype to production, including logging, error handling, rate limits, and fallback strategies
• Collaborating closely with product and stakeholders to ship reliable, user-facing AI features
• Contributing to system design decisions and engineering best practices.

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