Job Openings at Pavago

About the Role:

• Our client is seeking a Budgeting/Forecasting Specialist to build financial models, manage planning cycles, and provide forward-looking insights that guide strategy. This role requires strong analytical skills, mastery of modeling tools, and the ability to translate numbers into actionable recommendations. You will work closely with finance leaders and department heads to ensure budgets and forecasts are accurate, realistic, and strategically aligned.

Responsibilities:

Budget Development:

• Lead preparation of annual operating and capital budgets.
• Consolidate inputs across departments and business units.
• Ensure budgets align with company goals and resource constraints.

Forecasting:

• Maintain rolling forecasts for revenue, expenses, and cash flow.
• Update models monthly or quarterly with actuals, adjusting assumptions.
• Build scenario and sensitivity analyses to test potential outcomes.

Variance Analysis:

• Compare actual results to budgets/forecasts.
• Prepare written commentary to explain variances and recommend corrective actions.

Financial Modeling:

• Build dynamic Excel or Google Sheets models (DCF, ROI, IRR, break-even).
• Use advanced formulas, macros, or BI tool integrations to improve accuracy.

Reporting:

• Prepare management reporting packages with dashboards and visualizations.
• Present insights to executives, highlighting risks and opportunities.

Collaboration:

• Partner with department heads to align forecasts with operational plans.
• Work with FP&A teams to refine planning assumptions.

Process Improvement:

• Identify inefficiencies in forecasting workflows.
• Implement new FP&A software or improve data integrations (Adaptive Insights, Anaplan, Workday, or equivalent).

What Makes You a Perfect Fit:

• Highly analytical with an eye for detail.
• Comfortable building models from scratch and explaining them simply.
• Proactive in identifying trends, risks, and opportunities.
• Skilled communicator who can influence leaders through data-driven storytelling.

Required Experience & Skills (Minimum):

• Bachelor’s degree in Finance, Accounting, Economics, or related field.
• 3+ years in financial planning/analysis, budgeting, or forecasting.
• Advanced Excel/Google Sheets (nested formulas, pivot tables, sensitivity modeling).
• Experience preparing variance analyses for leadership.

Ideal Experience & Skills:

• FP&A tool experience (Anaplan, Adaptive Insights, Hyperion, Workday).
• MBA, CFA, or CPA credential.
• Experience forecasting for SaaS, real estate, or professional services.
• Familiarity with BI/analytics platforms (Power BI, Tableau, Looker).

What Does a Typical Day Look Like?

• A Budgeting/Forecasting Specialist’s day revolves around building clarity about the future of the business. You will:
• Maintain financial models — updating them with actuals and revising assumptions based on market or internal trends.
• Analyze variances — reviewing how actual results differ from plan and preparing written explanations with supporting data.
• Support decision-making — running scenario analyses (“what happens if revenue grows 15%?” “what if headcount increases by 20?”) and presenting implications to leadership.
• Collaborate across teams — meeting with department managers to validate budget assumptions and ensure spending aligns with strategy.
• Deliver reporting — packaging forecasts and analyses into dashboards and executive-ready presentations.
• Continuously improve — refining models, implementing forecasting software, and streamlining inputs to shorten cycles.
• In essence: the role is about ensuring the organization has reliable forward-looking data to guide strategy, supported by strong financial models and clear commentary.

Key Metrics for Success (KPIs):

• Forecast accuracy within ±5–10%.
• Budget completion within agreed timelines.
• Variance explanations delivered within 2–3 days of close.
• Models updated regularly and free from errors.
• Forecasting cycle time is reduced via automation.

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About the Role
Our client is seeking a Full-Stack AI Engineer to design, build, and deploy AI-powered applications that bridge modern software engineering with applied machine learning. This role focuses on taking AI solutions from prototype to production — ensuring systems are scalable, reliable, secure, and optimized for real-world business impact.

• The ideal candidate combines strong full-stack engineering skills with hands-on experience integrating LLMs, machine learning models, vector databases, and AI workflows into production environments. You will work closely with product, engineering, and data teams to build intelligent applications that improve automation, user experience, and operational efficiency.
• This is a highly technical, execution-focused role for someone comfortable owning AI systems end-to-end — from infrastructure and APIs to front-end experiences and deployment pipelines.

Responsibilities

AI Model Integration & Deployment

•  Deploy and integrate pre-trained and fine-tuned ML/LLM models using platforms such as OpenAI, Hugging Face, TensorFlow, and PyTorch
•  Build scalable inference APIs using FastAPI, Flask, Node.js, or similar frameworks
•  Implement vector search and retrieval systems using Pinecone, Weaviate, FAISS, or ChromaDB
•  Design and optimize Retrieval-Augmented Generation (RAG) pipelines for AI-powered applications
•  Monitor model accuracy, latency, and operational performance in production environments

Data Engineering & AI Pipelines

•  Build ETL pipelines for ingesting, cleaning, transforming, and processing structured and unstructured datasets
•  Automate data preprocessing, labeling, validation, and versioning workflows
•  Manage datasets and pipelines using Airflow, Prefect, Dagster, or similar orchestration tools
•  Store and manage datasets in cloud data warehouses such as BigQuery, Snowflake, or Redshift
•  Optimize pipelines for scalability, reliability, and cost efficiency

Full-Stack Application Development

•  Build front-end interfaces in React, Next.js, or Vue for AI-powered features such as chatbots, dashboards, search, and analytics tools
•  Develop scalable back-end services and microservices that connect AI models to business logic
•  Ensure applications are responsive, secure, intuitive, and production-ready
•  Design APIs and services that support high concurrency and scalable AI workloads

Infrastructure, DevOps & Deployment

•  Containerize services using Docker and deploy workloads to Kubernetes environments
•  Build and maintain CI/CD pipelines for application and model deployments
•  Monitor infrastructure health, inference latency, system uptime, and operational costs
•  Implement observability and monitoring using MLflow, Weights & Biases, Datadog, Prometheus, or custom dashboards
•  Optimize AI inference performance and infrastructure costs across environments

Security & Compliance

•  Ensure AI systems comply with GDPR, HIPAA, SOC 2, and other applicable data privacy standards
•  Implement secure authentication, access controls, rate limiting, and API security best practices
•  Maintain secure handling of sensitive user and business data

Collaboration & Product Development

•  Work closely with data scientists to productionize experimental models and prototypes
•  Partner with product and engineering teams to scope and prioritize AI-driven features
•  Contribute to architecture discussions and technical planning
•  Document workflows, APIs, infrastructure, and AI systems for maintainability and reproducibility

What Makes You a Perfect Fit

•  Strong engineer with hands-on experience across both software development and applied AI/ML
•  Comfortable moving quickly from experimentation to production deployment
•  Analytical problem solver who balances scalability, latency, usability, and cost
•  Curious and adaptable, constantly exploring emerging AI frameworks, tools, and workflows
•  Ownership-driven with the ability to independently execute complex technical initiatives
•  Strong communicator capable of collaborating across technical and non-technical teams

Required Experience & Skills

•  3+ years of software engineering experience with exposure to AI/ML systems
•  Strong proficiency in Python and JavaScript/TypeScript
•  Hands-on experience with AI/ML frameworks such as PyTorch, TensorFlow, Hugging Face, or OpenAI APIs
•  Experience building scalable APIs and back-end systems
•  Front-end development experience using React, Next.js, Vue, or similar frameworks
•  Experience deploying machine learning models into production systems
•  Strong SQL skills and experience with cloud data warehouses
•  Familiarity with Docker, Kubernetes, and CI/CD workflows
•  Experience integrating APIs, vector databases, and AI inference services

Ideal Experience & Skills

•  Experience building and scaling AI-powered SaaS applications
•  Hands-on experience with embeddings, fine-tuning, and RAG pipelines
•  Familiarity with MLOps platforms such as MLflow, Kubeflow, Vertex AI, or SageMaker
•  Experience with serverless architectures and microservices
•  Knowledge of prompt engineering and AI workflow optimization
•  Experience optimizing inference latency and AI infrastructure costs
•  Familiarity with monitoring model drift, evaluation metrics, and AI observability practices

What Does a Typical Day Look Like?
A Full-Stack AI Engineer’s day revolves around building and optimizing production-grade AI systems. You will:

•  Develop and refine APIs that expose AI and LLM functionality
•  Build front-end interfaces that surface AI-powered workflows to end users
•  Maintain and optimize ETL pipelines for AI model training and inference
•  Deploy updates through CI/CD pipelines and monitor production performance
•  Troubleshoot latency, scaling, or infrastructure bottlenecks
•  Collaborate with product and data teams to prioritize impactful AI features
•  Document systems and workflows to ensure scalability and maintainability

In essence: you are responsible for turning AI capabilities into reliable, scalable, and user-friendly production applications.

Key Metrics for Success (KPIs)

•  Successful deployment of AI-powered features on schedule
•  Application uptime ≥ 99.9%
•  Inference latency maintained below target thresholds
•  Reliability and scalability of AI systems in production
•  Reduction in manual workflows through automation and AI integration
•  Stable model performance and monitoring accuracy over time
•  Positive adoption and usage of AI-driven features by end users
•  Infrastructure and inference cost optimization improvements

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About the Role

• Our client is seeking a Business Development Representative (BDR) to generate, qualify, and nurture high-quality sales opportunities. This role is responsible for engaging inbound and outbound leads, conducting discovery conversations, building relationships with prospects, and passing well-qualified opportunities to Account Executives.
• The ideal candidate is a strong communicator who combines persistence with professionalism and understands how to balance high-volume outreach with thoughtful qualification. You will play a critical role in driving pipeline growth while ensuring prospects receive a positive and consultative early sales experience.
• This is an ideal opportunity for someone who enjoys outbound prospecting, relationship building, and contributing directly to revenue growth in a fast-paced B2B sales environment.

Responsibilities
Lead Qualification & Discovery

• Engage inbound leads generated through marketing campaigns, website forms, referrals, and events
• Conduct discovery calls to understand prospect needs, budget, timeline, and decision-making process
• Qualify opportunities using frameworks such as BANT, MEDDIC, SPICED, or SPIN
• Identify pain points and business challenges to determine sales readiness
• Maintain detailed qualification notes and opportunity updates within the CRM

Outbound Prospecting & Pipeline Generation

• Identify and research target accounts using LinkedIn Sales Navigator, Apollo, ZoomInfo, or similar tools
• Execute outbound prospecting campaigns through email, phone, LinkedIn, and multi-channel outreach
• Personalize messaging based on company insights, ICP alignment, and prospect pain points
• Build and maintain a consistent pipeline of qualified opportunities for Account Executives

Pipeline Nurturing & Relationship Building

• Maintain communication with early-stage or not-yet-ready prospects through follow-up campaigns
• Share relevant resources, case studies, and value-driven messaging to nurture relationships
• Re-engage cold leads and route warm opportunities back into active sales conversations
• Build trust with prospects while maintaining a consultative sales approach

Sales Collaboration & Handoff

• Schedule demos, meetings, and discovery sessions for Account Executives
• Prepare detailed handoff notes, qualification summaries, and context for sales teams
• Participate in pipeline review meetings with sales leadership and AEs
• Collaborate with sales and marketing teams to improve targeting, messaging, and conversion performance

CRM & Reporting

• Maintain accurate records within Salesforce, HubSpot, Zoho, or similar CRM platforms
• Track lead stages, outreach activity, conversion metrics, and opportunity outcomes
• Ensure CRM hygiene and complete documentation across all opportunities
• Report weekly on outreach activity, pipeline generation, and qualification performance

Feedback & Continuous Improvement

• Share prospect feedback and objections with marketing and leadership teams
• Continuously optimize outreach messaging and qualification approaches
• Stay current on industry trends, prospect pain points, and competitor positioning

What Makes You a Perfect Fit

• Strong communicator who asks thoughtful questions and actively listens
• Consultative and relationship-focused rather than transactional
• Organized, metrics-driven, and highly disciplined with follow-through
• Comfortable with high-volume outreach while maintaining personalization and quality
• Resilient, coachable, and motivated by targets and pipeline growth
• Confident operating independently in a remote sales environment

Required Experience & Skills

• 2+ years of experience in BDR, SDR, inside sales, or outbound prospecting roles
• Experience qualifying leads through structured discovery conversations
• Proficiency with CRM systems such as Salesforce, HubSpot, or Zoho
• Experience using sales engagement and prospecting tools such as Outreach, SalesLoft, Apollo, or LinkedIn Sales Navigator
• Strong written and verbal English communication skills
• Ability to manage multiple conversations and follow-up sequences simultaneously

Ideal Experience & Skills

• 3–5 years of BDR or outbound sales experience with quota attainment history
• Experience selling into B2B SaaS, technology, professional services, or high-ticket industries
• Familiarity with sales methodologies such as MEDDIC, Challenger, SPIN, or Sandler
• Experience supporting mid-market or enterprise sales cycles
• Background in multi-channel outbound campaigns and account-based outreach strategies

What Does a Typical Day Look Like?

A Business Development Representative’s day revolves around creating and qualifying new pipeline opportunities. You will:

• Respond to inbound leads and conduct discovery conversations
• Build prospect lists and execute outbound outreach campaigns
• Personalize emails, LinkedIn messages, and call strategies for target accounts
• Conduct qualification calls and identify sales-ready opportunities
• Maintain detailed CRM records and update pipeline stages
• Collaborate with Account Executives on handoffs and sales strategy
• Review activity metrics and optimize outreach performance daily
• In essence: you ensure the sales team consistently receives high-quality, well-qualified opportunities backed by thoughtful discovery and professional prospect engagement.

Key Metrics for Success (KPIs)

• Daily and weekly outreach activity levels (calls, emails, LinkedIn touches)
• Discovery calls completed per week
• Qualified opportunities generated and passed to Account Executives
• Conversion rate from lead → discovery → qualified opportunity
• Pipeline contribution and sourced revenue opportunities
• CRM accuracy and completeness of documentation
• Response rates and outbound engagement performance