Neurons Lab

• Lead AI enablement across the group • Conduct assessments: run structured assessments with each business team to map workflows, skills, and current AI usage before any enablement is planned. • Find and solve pain points: collect the team's real pain points and use cases, prioritize them, and turn them into concrete enablement plans, working AI skills, or recommendations for specialist tools. • Deliver concrete outcomes: ensure every workshop targets the team's own use cases and produces walk-away artifacts: working skills, prompts, and tools the team uses the next day. • Activate champions: identify champions inside each team, work with them 1:1, and make their results visible to client leadership so momentum is seen at the top. • Assess and track adoption KPIs: set realistic adoption targets per team, measure actual usage after each enablement cycle, and report progress transparently to the client and internally.

• Join joint working sessions with the client's hands-on security engineers; challenge and harden their AI-driven offensive pipeline end-to-end (recon → verification → AI-planned exploitation → sandboxed execution). • Design and refine the exploitation agent: how the LLM plans attack paths, selects and validates exploits, and orchestrates parallel sandboxes safely and reproducibly. • Optimise cost-per-finding of the existing exploitation pipeline: benchmark local / sovereign open models (Kimi, GPT-OSS, MiniMax, DeepSeek) against frontier models for the recon, exploitation and analysis loops; quantify accuracy / latency / cost trade-offs and recommend hardware sizing. • Shape the runtime anomaly-detection layer: define which intrusion / privilege-escalation precursor patterns are worth collecting (signal over raw-log volume), and design the missing pieces — automated response (kill a malicious process / disable an account on detection) and triage routing by criticality. • Stand up a quick-win PoC to anchor the engagement — e.g. an automated dependency / PR vulnerability-scanning pass, or a head-to-head local-vs-frontier benchmark of the exploitation agent. • Turn findings into a defensible technical proposal and roadmap; present methodology and trade-offs to a technical CISO / CTO audience. • Keep all sensitive work build-time and in-perimeter — no pushing intellectual property, configs, or recon-enabling data to external model providers; respect regulated-gaming certification constraints (no uncertified AI in runtime-critical paths).

• Profile the anonymized lake hands-on — interrogate tens-of-millions-of-row tables and reproduce and validate the team's existing descriptive statistics, so every number is traceable to source. • Build and validate the core risk models yourself: PD, delinquency / roll-rate, early-warning, segmentation and scorecards. • Stand up the model-validation discipline that makes outputs audit-defensible: train / test / out-of-time splits, Gini / AUC / KS, calibration, stability (PSI), backtesting and full model documentation. • Define feature logic with the Data Engineer and write it yourself in SQL / dbt / Python; specify the harmonized definitions the semantic layer must serve. • Prototype and validate the natural-language insight layer; check answer correctness and add guardrails. • Run a credit-policy / cut-off analysis showing where the client could tighten policy or reduce delinquency — the concrete insight their own clients keep asking for. • Lead a small pod (Data Engineer, client's junior offshore data people): set tasks, review work, be the quality bar and the human-in-the-loop. • Front the client's data leadership: present findings, explain methodology to non-technical executives, and shape the phased roadmap / SoW.

• Reproduce a descriptive-statistics report end-to-end so any figure traces back to raw source — closing the gap the client admitted (numbers they can't currently defend). • Profile and reconcile differing source schemas across acquired entities: map differing field names, types, encodings and business definitions for the same concept into one conformed model. • Build dbt staging → intermediate → mart models with tests; codify the harmonized definitions the Data Science Lead specifies. • Write Great Expectations suites (null / range / uniqueness / referential checks) and wire them into the pipeline so bad data fails loudly rather than silently corrupting analysis. • Implement entity / identity resolution (deterministic + fuzzy matching) where there is no clean shared key for the same customer or account across sources. • Implement and verify anonymization / pseudonymization (hashing / tokenization / k-anonymity) and evidence that re-identification risk is controlled for the client's IT / compliance team. • Optimize Spark / Glue jobs over tens of millions of rows — partitioning, file formats (Parquet), incremental loads, cost control. • Orchestrate with Airflow / Step Functions; build repeatable, scheduled pipelines rather than one-off scripts. • Prepare clean, documented, feature-ready datasets for the PD / delinquency models. • Document runbooks so the offshore team can operate the pipelines and handover takes days, not weeks; help scope onboarding of the remaining (Ireland + additional) sources.

• Identify and validate high-value AI opportunities • Rapidly prototype solutions • Ensure implementations deliver measurable business outcomes • Facilitate stakeholder workshops to identify and prioritize AI use cases • Develop business cases with clear ROI models • Document current workflows and pain points • Design workflows for financial environments • Create user stories and acceptance criteria • Build functional demonstrations using no-code/low-code tools • Develop testing methodologies for AI systems in regulated environments • Manage stakeholder expectations throughout delivery • Stay current on AI capabilities and new tools.

Role Description Scale Neurons Lab's delivery organization into a predictable engine for client value and expansion revenue — leading a team of Senior ADMs, owning client relationships at the executive level, and positioning Neurons Lab as a premier AI partner for enterprise clients. - KPI: Expansion revenue ≥ 50% of total revenue - KPI: PoC-to-Production conversion rate > 30% - KPI: 90%+ of project issues resolved at team level without CDO escalation - KPI: Average deal size $50k+ - KPI: Client satisfaction (CSAT) consistently maintained and improved Qualifications - Delivery Leadership — experience running and scaling multi-team delivery organizations. - Account Management — multi-threaded executive relationships, expansion playbook, revenue ownership. - Customer Success — portfolio-level health management, value realization, escalation, renewals. - Stakeholder Management — navigates complex enterprise environments at board and C-suite level. - Executive Communication — credible in front of CEOs, CIOs, CTOs, COOs; decks as strategic artifacts. - People Management — proven ability to recruit, develop, and let go; builds high-performing teams. - AI/ML Awareness — conversant with engineering and data science teams; must hold their own technically. - Change Management — understands how to drive adoption of AI products inside enterprise clients. Requirements - Senior delivery or account leadership (7+ years) — customer success, engagement management, or consulting at a leadership level. - Team management (3+ years) — built or led delivery teams; track record of developing talent. - Business development — proven expansion revenue track record through delivery quality and consultative selling. - Regulated industry experience — direct enterprise client work in FSI, healthcare, pharma, energy, or government. - AI/ML or data product exposure — conversant with delivery teams on complex technical engagements. - Advanced English — exceptional written and verbal; comfortable leading executive conversations. Nice to have - AI consulting or systems integrator background. - Change management certification (Prosci/ADKAR, Kotter). - Experience managing partner or subcontractor delivery relationships. Benefits - Competitive compensation — a monthly base plus expansion revenue upside. - Fully remote — outcomes over attendance. - Unlimited PTO. - Full-time contractor engagement with a fast-growing AI consultancy at the forefront of enterprise transformation.

• Join Neurons Lab as a Cloud Engineer on a delivery engagement with a regulated EU BFSI enterprise (German-speaking client). • You will pick up a CDK-based codebase already deployed inside the client's AWS account, take over from the outgoing engineer, and own cloud delivery end-to-end: production hardening, security findings remediation, RAG infrastructure stability, and SSO/RBAC integration with the client's identity stack. • Report to AI Architect on the engagement; collaborate day-to-day with the AI Delivery Manager and ML Engineer. • Own and extend the existing AWS CDK codebase deployed inside the client's AWS account. • Operate the production stack: ECS Fargate, ECR, ALB (public + internal), VPC, CDN, S3, AWS Bedrock. • Run the data layer: Postgres, Redis, vector database (Qdrant or similar), LLM observability (Langfuse or similar). • Triage and remediate AWS Security Hub / Health Dashboard findings independently. • Integrate SSO and RBAC with the client's identity stack. • Keep the RAG stack reliable as additional pilot teams onboard; partner with the ML Engineer on retrieval-quality incidents. • Own cost tracking and capacity planning for the client's Bedrock + ECS spend. • Document CDK constructs, runbooks, and incident playbooks for effective handover.

• Support business development through technical expertise and client communication • Enable engineering team growth and high-performance delivery • Contribute to critical AI system architecture and implementation • Achieve 90%+ Customer Satisfaction Index (CSI) on technical delivery • Support team performance improvement and capability development • Deliver scalable AI system architectures that meet FSI compliance requirements • Communicate project progress with customers, explaining business and technology logic clearly • Prepare upsell and account expansion ideas for existing clients • Assist in proposal preparation for new client engagements • Lead AI Engineers on customer projects, create tasks, control performance, and share feedback (not all projects require AI Engineers) • Help the engineering team grow, identify, and support high-performers • Participate in performance reviews and performance improvement plans • Take part in the implementation of critical software pieces • Define how AI transforms business processes; design end-to-end AI-powered experiences • Design scalable AI systems architecture; decide on model selection, deployment patterns, and infrastructure requirements • Bridge business stakeholders and technical teams; translate business needs into technical specifications • Design how multiple AI agents/models work together; define agent-to-agent communication protocols • Establish AI development standards, safety protocols, and compliance frameworks • Design systems for AI safety, bias mitigation, and failure modes; implement monitoring and intervention systems

• Ensure accurate, reliable, and efficient financial operations of the company, while maintaining strong financial control, compliance, and visibility for decision-making. • Own invoicing, billing, payments, and bookkeeping oversight • Prepare monthly P&L, cash flow, and budget tracking • Monitor financial performance vs targets • Manage cash flow forecasting and runway visibility • Oversee banking, payments, and liquidity • Improve and standardize financial processes • Ensure clean data across finance tools (e.g. HubSpot, QuickBooks) • Coordinate with accountants, tax advisors, and auditors • Ensure timely filings and regulatory compliance • Support contract review (pricing, margins, payment terms)

• Generate a predictable new revenue pipeline • Achieve quarterly sales targets • Establish Neurons Lab as the preferred AI partner for Financial Services enterprises • Create a high-velocity sales engine through AI-powered processes and strategic partnerships • Generate 12+ qualified opportunities per quarter (SQLs) • Maintain >40% MQL to SQL conversion rate • Achieve average deal size >$50,000 • Own full sales cycle from first contact to closed-won • Apply MEDDIC methodology for opportunity qualification • Achieve >25% win rate on qualified opportunities (SQLs) • Execute multi-channel campaigns targeting C-suite and VP-level executives • Articulate business value and ROI for AI initiatives • Maintain accurate CRM data and pipeline forecasts