Do work that matters.  

At AlertMedia, everything we do supports our mission: To save lives and minimize loss by identifying active threats globally and facilitating timely communications when an emergency threatens personal safety and business continuity.    

Our core values drive us in our important mission of keeping people safe & informed:   

• We’re humans not robots  
• Customers always come first  
• We work better together  
• Simplicity is our strength  
• Our reputation is priceless  
• Hard work pays off 

As one of the fastest growing software companies in the nation, we’re focused on finding the best talent and building the best team to continue accelerating our rapid growth to keep up with our demand. 

AlertMedia is looking for an experienced Database Engineer (Search Platform) with deep expertise in Elasticsearch at scale, relational databases (Postgres/MySQL), and modern search architectures including vector search, semantic search, hybrid search, and ranking strategies such as Reciprocal Rank Fusion (RRF).

This is a hands-on role focused on designing, operating, and evolving the data stores and search infrastructure that power AlertMedia’s real-time risk intelligence platform. You will play a critical role in how we index, query, rank, and retrieve signals across large-scale structured and unstructured datasets. We’re looking for someone who has built and operated production-grade search and database systems, understands distributed systems tradeoffs, and is comfortable owning complex data infrastructure end to end. We want you to shine by showing us real systems you’ve designed, clusters you’ve scaled, search architectures you’ve improved, and performance problems you’ve solved. We don’t do trick questions or whiteboard exercises.

Who you are:    

You are a pragmatic database engineer who thrives in a collaborative environment and enjoys working on systems that operate at scale in the real world.
You have significant experience designing, tuning, and operating Elasticsearch clusters in production, including large datasets, high ingest rates, and low-latency search requirements. You understand shard allocation, index lifecycle management, query optimization, cluster scaling, replication, and performance tuning. You also have strong experience with relational databases like Postgres or MySQL, including schema design, indexing strategies, query planning, and operational best practices.

You care deeply about:

• Building resilient, observable, and scalable data infrastructure
• Search relevance, performance, and correctness
• Data modeling and storage design
• Operational excellence and production reliability
• Clean, maintainable infrastructure and automation

You are comfortable working in Python and SQL, have experience with AWS infrastructure, and enjoy improving systems continuously.

What you get to every day:    

• Design, scale, and operate Elasticsearch clusters supporting large-scale ingest and low-latency query workloads
• Architect and optimize hybrid search systems, combining keyword search (BM25) with vector/semantic retrieval
• Implement and tune vector search, semantic search pipelines, and ranking strategies (e.g., RRF, rescoring, custom ranking logic)
• Improve indexing strategies, shard design, mappings, analyzers, and query performance
• Design and maintain relational database systems (Postgres/MySQL) supporting application and data workloads
• Partner with data engineering teams to ensure ingestion pipelines are optimized for search and database performance
• Monitor, troubleshoot, and optimize cluster health, capacity planning, scaling strategies, and cost efficiency
• Improve observability, alerting, backup/restore, disaster recovery, and operational best practices
• Contribute to architecture decisions around storage, indexing, retrieval, and relevance
• Operate in an Agile environment using tools like Git, JIRA, Jenkins, and Slack
• Help shape AlertMedia’s long-term data platform and search strategy
• Contribute to AlertMedia’s culture, values, and long-term technical vision

What you bring to the role: 

• Bachelor’s degree in Computer Science, Engineering, or related field (preferred)
• 3-4+ years of experience in database engineering, search engineering, or backend data platform roles
• Expert level hands-on experience with Elasticsearch at scale (cluster design, tuning, scaling, and operations)
• Experience designing and implementing vector search, semantic retrieval, hybrid search, and ranking strategies such as RRF
• Experience with AWS or other cloud platforms (required)
• Solid experience with relational databases such as Postgres or MySQL, including schema design and query optimization
• Strong understanding of search relevance concepts (BM25, analyzers, scoring, boosting, rescoring)
• Experience with high-throughput ingest systems and indexing pipelines
• Proficiency in Python and SQL 
• Strong organizational skills and ability to manage multiple pipelines and priorities
• Excellent written and verbal communication skills, with the ability to collaborate cross-functionally
• Ability and desire to work in a fast-paced, technically challenging environment
• English proficiency (written and oral) is required

Nice to Have

• Broader data engineering experience (data acquisition, ETL, streaming pipelines, Kafka/Kinesis/SQS)
• Experience with AI/ML pipelines, embeddings generation, or MLOps workflows
• Experience operating vector databases or ANN systems (FAISS, Milvus, OpenSearch vector, pgvector, etc.)
• Experience with large-scale data processing frameworks 
• Please note: AlertMedia does not sponsor applicants for work visas.

Why you’ll love working at AlertMedia:

• Competitive Base Salary (Bi-monthly payroll)
• Annual Company Bonus
• 100% Employer-Paid Medical, Dental, and Vision Coverage
• Parental Leave Program 
• Christmas Bonus (Aguinaldo)
• Generous Vacation and Holiday Policy
• Additional Vacation Bonus
• Monthly Work-from-Home Stipend
• All Necessary Equipment Provided: Laptop, monitor and accessories
• Recognition & Rewards: We celebrate wins big and small
• Language Immersion Program with employees spread out across the globe (English, Spanish, etc...depending on your interest)
• Continuous Learning & Growth: From mentorship and tech talks to personalized 1:1 career development sessions, we’re deeply committed to investing in your professional journey
• Award Winning Culture: We're proud to be recognized as a BEST WORKPLACE FOR 10 YEARS IN A ROW
• The Work & People: We’re passionate about the meaningful work we do—and even more grateful for the incredible people we get to do it with!

About AlertMedia:

AlertMedia helps organizations protect their people and businesses through all phases of an emergency. Our award-winning threat intelligence, emergency communication, and travel risk management solutions help companies of all sizes identify, respond to, and recover from critical events faster and more confidently. AlertMedia supports essential communication for thousands of leading businesses—including JetBlue, Coca-Cola Bottling, and Walmart—in more than 150 countries.

We are an equal opportunity employer focused on creating a collaborative and exciting place for all to work. Ensuring a diverse, inclusive, and equitable workplace for all people is key to our success and core to our values.   All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status. 

This is an amazing opportunity to be part of our wave of momentum and take our company, and your career, to the next growth stage. We'd love to get to know you better and share how we serve our amazing customers. For more information, please visit www.alertmedia.com.   

Come join us in our mission to save lives and minimize loss through effective communication.  

By applying for the role, you agree that Alert Media will use your Personal Information in connection with the recruitment process and in accordance with our Privacy Policy – https://www.alertmedia.com/legal/privacy-policy/

What are we looking for?

We are seeking a highly skilled and experienced Senior DevOps/Cloud Engineer/SRE with 7+ years of experience to become an integral part of our dynamic DevOps platform team. In this role, you will be instrumental in supporting cutting-edge AI SaaS and PaaS solutions.

Please note: This position may require participation in an on-call support rotation in the future.

Requirements

Technical Skills:

• Education: A Bachelor's degree in Computer Science or a related field, or equivalent education and experience equating to the US equivalent of a bachelor’s degree.
• SDLC & CI/CD: 5+ years of experience with Software Development Lifecycle (SDLC) methodologies and CI/CD technologies, including hands-on expertise with at least two different CI/CD ecosystems. Optional: We also value experience with MLOps practices and their integration into CI/CD pipelines.
• Software Engineering: 3+ years of experience as a Software Engineer, with a preference for backend development experience. Nice to have: Experience with Temporal workflows.
• Networking & Operating Systems: Strong knowledge of networking principles and robust experience with Operating System concepts, backed by 5+ years of hands-on Linux administration.
• Cloud Platforms: 5+ years of in-depth experience with AWS (highly preferred) and/or GCP. Highly desired to have certifications in AWS or GCP.
• Infrastructure as Code (IaC): 5+ years of experience with IaC tools, primarily Terraform.
• Kubernetes: 5+ years of experience managing Kubernetes clusters, including practical experience with at least one cloud-managed service (e.g., EKS, GKE, AKS).
• Database Management: Strong experience with PostgreSQL.
• Troubleshooting: Proven ability to effectively troubleshoot issues across code, cloud infrastructure, networks, and operating systems.
• AI-Specific Technologies (Preferred): Given our focus on AI solutions, experience with MLOps, Nvidia NIM and relevant Nvidia certifications would be a significant asset.

Observability: 5+ years of experience working with observability tools, and defining

Essential Non-Technical Skills:

• Requirements Elicitation: Ability to effectively gather and understand project requirements.
• Collaboration & Resilience: A highly collaborative team player with a resilient and proactive mindset.
• Organizational & Communication Skills: Self-organized with the ability to structure discussions effectively, ensuring focus and clarity.
• Documentation: Meticulous and thorough in creating clear, comprehensive documentation.
• Problem Solving: Adept at breaking down complex problems into manageable sub-tasks and defining actionable next steps.
• Language: English proficiency is mandatory.

#LI-Remote

#LI-DC1

The salary range for this role is $50,000 - $120,000 per year (Gross in USD) 

About Sezzle:

With a mission to financially empower the next generation, Sezzle is revolutionizing the shopping experience beyond payments, blending cutting-edge tech with seamless, interest-free installment plans that make shopping smarter and more accessible. We’re not just transforming payments; we’re redefining how people discover, interact with, and purchase the things they love while driving real impact on merchant sales through increased conversions and higher order values. As we continue to shape the future of fintech and retail, we’re building an innovative, dynamic team passionate about creating more than just a transaction but a truly unique shopping journey. If you’re excited about pushing boundaries in tech and delivering a game-changing experience for consumers and merchants alike, come join us at Sezzle and help create the future of shopping!

About the Role: 

We are seeking a talented and motivated  Sr. AI engineer who is best in class with a high IQ plus a high EQ. This role presents an exciting opportunity to thrive in a dynamic, fast-paced environment within a rapidly growing team, with abundant prospects for career advancement.

You will be responsible for overseeing the design, development, and deployment of machine learning models that power and enhance our financial platform. In this role, you will drive the creation of scalable machine learning solutions for personalized recommendations in the Sezzle marketplace, fraud detection, and credit risk assessment, utilizing a combination of cloud services, open-source tools, and proprietary algorithms.

Your leadership will be key in blending machine learning development and operations (MLOps) to automate and optimize the full lifecycle of our ML models. You will collaborate with a team of engineers and data scientists to build large-scale, high-quality solutions that address diverse challenges in the shopping and fintech space. You’ll ensure our AI-driven features are robust, efficient, and scalable as we continue to grow.

What You'll Do:

• Design, Build, and Maintain Scalable ML Infrastructure: Lead the design and development of scalable machine learning infrastructure on AWS, utilizing services like AWS Sagemaker for efficient model training and deployment.
• Collaborate with Product Teams: Work closely with product teams to develop MVPs for AI-driven features, ensuring quick iterations and market testing to refine solutions effectively.
• Develop Monitoring & Alerting Frameworks: Create and enhance monitoring and alerting systems for machine learning models to ensure high performance, reliability, and minimal downtime.
• Support Cross-Departmental AI Utilization: Enable various departments within the organization to leverage AI/ML models, including cutting-edge Generative AI solutions, for different use cases.
• Provide Production Support: Offer expertise in debugging and resolving issues related to machine learning models in production, participating in on-call rotations for operational troubleshooting and incident resolution.
• Scale ML Architecture: Design and scale machine learning architecture to support rapid user growth, leveraging deep knowledge of AWS and ML best practices to ensure robustness and efficiency.
• Mentor and Elevate Team Skills: Conduct code reviews, mentor team members, and elevate overall team capabilities through knowledge sharing and collaboration.
• Stay Ahead of the Curve: Stay updated with the latest advancements in machine learning technologies and AWS services, driving the adoption of cutting-edge solutions to maintain a competitive edge.

What We Look For:

• Bachelor's degree in Computer Science, Computer Engineering, Machine Learning, Statistics, Physics, or a relevant technical field, or equivalent practical experience.
• At least 6+ years of experience in machine learning engineering, with demonstrated success in deploying scalable ML models in a production environment.

Preferred Qualifications:

• Deep expertise in one or more of the following areas: machine learning, recommendation systems, pattern recognition, data mining, artificial intelligence, or related technical fields.
• Proven track record of developing machine learning models from inception to business impact, demonstrating the ability to solve complex challenges with innovative solutions.
• Proficiency with Python is required, and experience with Golang is a plus.
• Demonstrated technical leadership in guiding teams, owning end-to-end projects, and setting the technical direction to achieve project goals efficiently.
• Experience working with relational databases, data warehouses, and using SQL to explore them.
• Strong familiarity with AWS cloud services, especially in deploying and managing machine learning solutions and scaling them in a cost-effective manner.
• Knowledgeable in Kubernetes, Docker, and CI/CD pipelines for efficient deployment and management of ML models.
• Comfortable with monitoring and observability tools tailored for machine learning models (e.g., Prometheus, Grafana, AWS CloudWatch) and experienced in developing recommender systems or enhancing user experiences through personalized recommendations.
• Solid foundation in data processing and pipeline frameworks (e.g., Apache Spark, Kafka) for handling real-time data streams.

About You:

• You have relentlessly high standards - many people may think your standards are unreasonably high. You are continually raising the bar and driving those around you to deliver great results. You make sure that defects do not get sent down the line and that problems are fixed so they stay fixed.
• You’re not bound by convention - your success—and much of the fun—lies in developing new ways to do things
• You need action - speed matters in business. Many decisions and actions are reversible and do not need extensive study. We value calculated risk-taking.
• You earn trust - you listen attentively, speak candidly, and treat others respectfully.
• You have backbone; disagree, then commit - you can respectfully challenge decisions when you disagree, even when doing so is uncomfortable or exhausting. You have conviction and are tenacious. You do not compromise for the sake of social cohesion. Once a decision is determined, you commit wholly.
• You deliver results - you focus on the key inputs and deliver them with the right quality and in a timely fashion. Despite setbacks, you rise to the occasion and never settle.

What Makes Working at Sezzle Awesome:

At Sezzle, we are more than just brilliant engineers, passionate data enthusiasts, out-of-the-box thinkers, and determined innovators. We believe in surrounding ourselves with only the best and the brightest individuals. Our culture is not defined by a certain set of perks designed to give the illusion of the traditional startup culture, but rather, it is the visible example living in every employee that we hire. 

#Li-remote #full-time

The salary range for this role is $50,000 - $120,000 per year (Gross in USD) 

About Sezzle:

With a mission to financially empower the next generation, Sezzle is revolutionizing the shopping experience beyond payments, blending cutting-edge tech with seamless, interest-free installment plans that make shopping smarter and more accessible. We’re not just transforming payments; we’re redefining how people discover, interact with, and purchase the things they love while driving real impact on merchant sales through increased conversions and higher order values. As we continue to shape the future of fintech and retail, we’re building an innovative, dynamic team passionate about creating more than just a transaction but a truly unique shopping journey. If you’re excited about pushing boundaries in tech and delivering a game-changing experience for consumers and merchants alike, come join us at Sezzle and help create the future of shopping!

About the Role: 

We are seeking a talented and motivated Sr. AI engineer who is best in class with a high IQ plus a high EQ. This role presents an exciting opportunity to thrive in a dynamic, fast-paced environment within a rapidly growing team, with abundant prospects for career advancement.

You will be responsible for overseeing the design, development, and deployment of machine learning models that power and enhance our financial platform. In this role, you will drive the creation of scalable machine learning solutions for personalized recommendations in the Sezzle marketplace, fraud detection, and credit risk assessment, utilizing a combination of cloud services, open-source tools, and proprietary algorithms.

Your leadership will be key in blending machine learning development and operations (MLOps) to automate and optimize the full lifecycle of our ML models. You will collaborate with a team of engineers and data scientists to build large-scale, high-quality solutions that address diverse challenges in the shopping and fintech space. You’ll ensure our AI-driven features are robust, efficient, and scalable as we continue to grow.

What You'll Do:

• Design, Build, and Maintain Scalable ML Infrastructure: Lead the design and development of scalable machine learning infrastructure on AWS, utilizing services like AWS Sagemaker for efficient model training and deployment.
• Collaborate with Product Teams: Work closely with product teams to develop MVPs for AI-driven features, ensuring quick iterations and market testing to refine solutions effectively.
• Develop Monitoring & Alerting Frameworks: Create and enhance monitoring and alerting systems for machine learning models to ensure high performance, reliability, and minimal downtime.
• Support Marketing Team’s AI Utilization with an eye on Cross-Departmental impact: Enable various departments within the organization to leverage AI/ML models, including cutting-edge Generative AI solutions, for different use cases.
• Provide Production Support: Offer expertise in debugging and resolving issues related to machine learning models in production, participating in on-call rotations for operational troubleshooting and incident resolution.
• Scale ML Architecture: Design and scale machine learning architecture to support rapid user growth, leveraging deep knowledge of AWS and ML best practices to ensure robustness and efficiency.
• Mentor and Elevate Team Skills: Conduct code reviews, mentor team members, and elevate overall team capabilities through knowledge sharing and collaboration.
• Stay Ahead of the Curve: Stay updated with the latest advancements in machine learning technologies and AWS services, driving the adoption of cutting-edge solutions to maintain a competitive edge.

What We Look For:

• Bachelor's degree in Computer Science, Computer Engineering, Machine Learning, Statistics, Physics, or a relevant technical field, or equivalent practical experience.
• At least 6+ years of experience in machine learning engineering, with demonstrated success in deploying scalable ML models in a production environment.

Preferred Qualifications:

• Deep expertise in one or more of the following areas: machine learning, recommendation systems, pattern recognition, data mining, artificial intelligence, or related technical fields.
• Proven track record of developing machine learning models from inception to business impact, demonstrating the ability to solve complex challenges with innovative solutions.
• Proficiency with Python is required, and experience with Golang is a plus.
• Demonstrated technical leadership in guiding teams, owning end-to-end projects, and setting the technical direction to achieve project goals efficiently.
• Experience working with relational databases, data warehouses, and using SQL to explore them.
• Strong familiarity with AWS cloud services, especially in deploying and managing machine learning solutions and scaling them in a cost-effective manner.
• Knowledgeable in Kubernetes, Docker, and CI/CD pipelines for efficient deployment and management of ML models.
• Comfortable with monitoring and observability tools tailored for machine learning models (e.g., Prometheus, Grafana, AWS CloudWatch) and experienced in developing recommender systems or enhancing user experiences through personalized recommendations.
• Solid foundation in data processing and pipeline frameworks (e.g., Apache Spark, Kafka) for handling real-time data streams.

About You:

• You have relentlessly high standards - many people may think your standards are unreasonably high. You are continually raising the bar and driving those around you to deliver great results. You make sure that defects do not get sent down the line and that problems are fixed so they stay fixed.
• You’re not bound by convention - your success—and much of the fun—lies in developing new ways to do things
• You need action - speed matters in business. Many decisions and actions are reversible and do not need extensive study. We value calculated risk-taking.
• You earn trust - you listen attentively, speak candidly, and treat others respectfully.
• You have backbone; disagree, then commit - you can respectfully challenge decisions when you disagree, even when doing so is uncomfortable or exhausting. You have conviction and are tenacious. You do not compromise for the sake of social cohesion. Once a decision is determined, you commit wholly.
• You deliver results - you focus on the key inputs and deliver them with the right quality and in a timely fashion. Despite setbacks, you rise to the occasion and never settle.

What Makes Working at Sezzle Awesome:

At Sezzle, we are more than just brilliant engineers, passionate data enthusiasts, out-of-the-box thinkers, and determined innovators. We believe in surrounding ourselves with only the best and the brightest individuals. Our culture is not defined by a certain set of perks designed to give the illusion of the traditional startup culture, but rather, it is the visible example living in every employee that we hire. 

#Li-remote #full-time

We’re looking for a highly skilled AI Engineer to help us design, build, and scale intelligent systems across our next-generation global HR Tech platform. As an early-stage startup, we’re looking for someone who thrives in dynamic environments, is passionate about automation and AI-driven innovation, and brings hands-on experience developing AI/ML solutions within enterprise-grade SaaS platforms.

You’ll work cross-functionally with product, data, and engineering teams to develop models that improve user experience, automate decision-making, and support smart HR and payroll solutions. This role reports directly to the Head of Engineering.

What You’ll Do

• Design and implement machine learning models to solve HR-specific challenges (e.g., intelligent automation, anomaly detection, personalized experiences)
• Collaborate with product managers and engineers to bring AI features to life across the platform
• Develop and optimize data pipelines to support model training and inference
• Research and apply NLP, LLMs, and other AI/ML technologies to streamline and improve HR workflows
• Monitor model performance and retrain/improve based on feedback and usage
• Build scalable AI solutions using cloud-native tools (preferably AWS)
• Contribute to data governance, ethical AI use, and compliance

What You’ll Bring

• 4–6+ years of experience as an AI/ML Engineer or Data Scientist, ideally with startup or SaaS exposure
• Strong understanding of supervised/unsupervised learning, NLP, neural networks, and generative AI
• Proficiency with Python and ML frameworks like TensorFlow, PyTorch, Hugging Face, or similar
• Experience deploying models in production using cloud platforms like AWS Sagemaker, GCP Vertex AI, or Azure ML
• Experience with data versioning, MLOps, and model lifecycle management
• Ability to work autonomously and thrive in a startup environment
• Familiarity with HR tech, payroll, or workforce data is a plus

The Mission

This is a senior role for someone who leads from both directions at once — deeply technical on customer engagements, and fully accountable for the growth and performance of a team of ML engineers and architects. You will report to the Director of AI/ML.

You own hiring, development, and team health alongside leading complex customer engagements, shaping architecture, and driving pre-sales. Both parts of this job are real and ongoing. The right candidate will find energy in that combination, not tension.

Your Assignment

Leading Your Team

• Hire and build: Set the technical bar for ML roles on your team, lead or oversee technical assessments, and make hiring decisions you can stand behind. Build a team that raises the practice's overall standard.
• Develop people: Run regular structured 1:1s, provide candid feedback at meaningful milestones, and actively invest in each person's growth — whether they are early in their career or highly experienced.
• Manage performance: Recognize strong contributors and address performance gaps directly and early. Partner with HRBPs and the Director of AI/ML when situations require a structured path, and advocate for your team when they deserve it.
• Stay close to staffing: Understand how your team is utilized across engagements, keep the staffing team informed of each person's skills evolution and preferences, and ensure people are placed in work that stretches them appropriately.

Strategic Advisory

• Lead ML assessments: Evaluate customer environments end-to-end — infrastructure, data pipelines, model lifecycle, and organizational readiness — and produce recommendations that drive executive decisions and open the door to the next engagement.
• Shape architecture: Serve as the senior technical authority on engagements, setting architectural direction, ensuring technical quality across the team, and making the calls that matter when tradeoffs are hard.
• Advise on ML operations: Help customers build ML systems they can actually own and sustain — translating MLOps, LLMOps, and production monitoring complexity into standards their engineering teams can execute and their leadership can act on.
• Drive pre-sales: Partner with sales and solutions teams during scoping and proposal phases, contributing the technical depth needed to scope work accurately and give prospects confidence in Caylent's ability to deliver.

Hands-On Delivery

• Lead engagements end-to-end: Drive architecture and solution design from kickoff through delivery — setting technical direction, unblocking the team on hard problems, and ensuring the work meets Caylent's quality standards.
• Own the technical relationship: Depending on the engagement, you are either the primary client contact owning all architect-level outcomes, or the senior technical authority providing oversight across the team. The expectation is the same in both cases — you are the person the engagement depends on technically.

Growing the Practice

• • Raise the bar internally: Mentor engineers and architects through real work, contribute to technical interviews, and build reference architectures and accelerators that make the broader ML practice better.

Your Qualifications (non-negotiables)

• 10+ years in machine learning or AI, with a proven track record of leading client-facing engagements in a consulting or advisory capacity.
• Demonstrated people management experience — hiring, performance calibration, career development, and the ability to have difficult conversations directly and constructively.
• Deep, current knowledge of the AWS ML and GenAI ecosystem, with the ability to make and defend architectural decisions across the full ML lifecycle — from data and feature engineering through training, deployment, and monitoring.
• Deep expertise in at least two or three ML domains — whether classical ML, computer vision, NLP, time series, or others — combined with the judgment to assess, architect, and advise across the broader ML landscape.
• Proven ability to architect and govern production ML systems end-to-end, translating MLOps, LLMOps, and broader AI operations complexity into standards that engineering teams can execute and executives can act on.
• Deep expertise across foundation model adaptation — fine-tuning (LoRA, QLoRA, PEFT), alignment (RLHF, DPO), inference optimization, and distributed training — combined with RAG and agentic system design, including multi-agent architectures, MCP integration, and human-in-the-loop patterns on AWS.
• Proven ability to operate independently in complex, ambiguous customer environments — navigating competing priorities, aligning stakeholders, and translating ML tradeoffs into business risk and value for both technical and executive audiences.

Strong differentiators

• AWS Certified Machine Learning – Specialty and/or AWS Certified Solutions Architect – Professional.
• Experience shaping practice-level standards, reference architectures, and reusable ML accelerators across multiple engagements.
• Exposure to varied industries and problem types in a consulting or client-facing context.
• Deep fluency in responsible AI practices — model evaluation, bias detection, fairness frameworks, and AI governance — applied in enterprise deployments.
• Fluency in AIOps patterns — designing agentic workflows for anomaly detection, automated root cause analysis, and remediation across observability platforms — and the ability to translate AI operations outcomes into measurable business value for customers.

Technical Stack

Our practice spans a broad range of ML domains. Candidates are expected to prescribe — not just recognize — with the judgment to maximize what AWS makes possible and the experience to know how open-source tooling strengthens it.

• ML Domains: Classical ML, Computer Vision, NLP, Generative AI & LLMs, AI Agents & Autonomous Systems, Intelligent Document Processing, Video Understanding, Speech & Audio, Time Series & Forecasting, Recommender Systems, Graph ML, Reinforcement Learning, Multimodal AI
• AWS ML Platform: SageMaker, SageMaker Pipelines, SageMaker Feature Store, SageMaker Model Registry, SageMaker Clarify, Bedrock (Agents, Knowledge Bases, Guardrails, AgentCore, Model Evaluation)
• Multi-provider LLM: Bedrock, Anthropic API, OpenAI API, Google Gemini API, Azure OpenAI — with the judgment to reason across provider tradeoffs in enterprise contexts
• AWS AI Services: Rekognition, Comprehend, Transcribe, Textract, Translate, Personalize, Neptune, Kinesis Video Streams, Polly
• Data Platform: Apache Spark / PySpark, Apache Kafka, Amazon Kinesis, Apache Iceberg, Delta Lake, Apache Hudi, AWS Glue
• Vector Databases: Pinecone, pgvector, Amazon OpenSearch (vector), Weaviate
• Frameworks: PyTorch, TensorFlow, JAX, Scikit-learn, XGBoost, HuggingFace (Transformers, PEFT, TRL), LangChain, LlamaIndex, DSPy, Ollama
• MLOps & Governance: MLflow, W&B, Airflow / MWAA (data orchestration), Dagster (asset-based pipelines), Kubeflow Pipelines, CI/CD, IaC (CloudFormation, CDK, Terraform), Docker, Kubernetes, ML Governance (lineage, data contracts, audit), Responsible AI / Bias & Fairness
• LLM Evaluation & Safety: RAGAS, LLM-as-judge patterns, DeepEval, NeMo Guardrails, Constitutional AI patterns, structured output validation
• Inference & Optimization: Triton, vLLM, SGLang, Trainium, Inferentia, Quantization (GPTQ, AWQ, bitsandbytes), SageMaker Neo

Benefits

• 100% remote work
• Medical Insurance for you and eligible dependents
• Generous holidays and flexible PTO
• Competitive phantom equity
• Paid for exams and certifications
• Peer bonus awards
• State of the art laptop and tools
• Equipment & Office Stipend
• Individual professional development plan
• Annual stipend for Learning and Development
• Work with an amazing worldwide team and in an incredible corporate culture

The Mission

We are seeking an exceptional Machine Learning Architect to join our growing Cloud Native Applications team. The right candidate is someone who has deep expertise in ML system design and is passionate about working with our customers, partners, and colleagues to drive innovation forward. Your mission will be to work alongside Caylent’s Engineers, Engineering Managers, and Project Managers to deliver AWS solutions across our diverse and forward-thinking customer base. You’ll work with the latest technologies and support customers looking to bring cutting-edge ideas to market.

Your Assignment

• You will be a mission control specialist, guiding Cayliens and Customers alike through Agile ceremonies like stand-ups, retrospectives, and more.
• You will translate customer requirements and into a workable backlog of tickets for engineers.
• Delegate tickets to a team of engineers in order to complete customer projects.
• Lead requirements gathering, backlog grooming, and architecture discussions.
• Apply your understanding of DevOps pipelines, including build automation, branching strategies, CI/CD, Infrastructure as Code, security, monitoring, logging, and alerting.
• Troubleshoot and resolve issues in customer dev, test, and production environments.
• Automate software testing at multiple levels (component, configuration item, subsystem, system) and monitor results.
• Write production quality code, including unit and integration tests. 
• Work with a team to deliver top-quality cloud native applications on AWS for customers.
• Clearly communicate and document your designs, processes, and procedures.
• You will demonstrate a passion to Stay Curious as you mentor peers, tackle new technologies, and learn from our word-class team of engineers.
• Coach and mentor less experienced teammates, coach and be mentored by world class engineers.

Your Qualifications

Core Consulting Qualifications 

Customer Experience 

• Confidently addresses client questions and identifies gaps in requirements with  proposed next steps. 
• Focus on delivering clear, actionable solutions that meet client goals

Collaboration 

• Facilitates alignment across stakeholders or technical priorities and deliverables.
• Navigates differing perspectives with professionalism to ensure shared understanding  and progress. 

Communication 

• Breaks down complex technical concepts for non-technical audiences in a clear, concise  manner. 
• Adapts communication style based on audience and context to improve clarity and decision-making. 

Technical Qualifications

• Experience with IaC tools (CloudFormation, CDK, Terraform).
• Expert level experience in Amazon SageMaker and ML libraries (TensorFlow, MXNet, PyTorch, Scikit-learn).
• Strong understanding of ML concepts: feature engineering, hyperparameter tuning, optimization strategies.
• Familiarity with MLOps tools (MLflow, Neptune, Comet).

Benefits 

• 100% remote work
• Medical Insurance for you and eligible dependents
• Generous holidays and flexible PTO
• Competitive phantom equity
• Paid for exams and certifications
• Peer bonus awards
• State of the art laptop and tools
• Equipment & Office Stipend
• Individual professional development plan
• Annual stipend for Learning and Development
• Work with an amazing worldwide team and in an incredible corporate culture

This role may require up to 25% travel, depending on business needs.

Perfil: Desarrollador Cloud

Rol: Desarrollador

Nivel: STD – SR

Experiencia

Años de experiencia:

• 3 o más años de experiencia como Desarrollador/a Cloud (Multi‑Nube) con IA/ML y DevOps.

Escolaridad

• Licenciatura o Ingeniería en:
• Sistemas
• Computación
• Matemáticas
• Física
• Carrera afín
• (O experiencia equivalente comprobable)

Modalidad y Condiciones de Trabajo

• Modalidad de trabajo: Híbrido
• Días en oficina: 2 veces por semana
• Horario: Lunes a viernes, 9:00 a.m. a 6:00 p.m.
• Ubicación: CDMX
• Dirección de sede: Polanco / Reforma

Objetivo del Puesto

Diseñar, construir y operar aplicaciones y servicios escalables en entornos multi‑nube (AWS, GCP y/o Azure), integrando desarrollo backend, prácticas DevOps/MLOps e implementación de modelos de IA/ML en entornos productivos, asegurando calidad, seguridad, observabilidad y eficiencia operativa.

Certificaciones Recomendadas

Google Cloud Platform

• Google Professional Cloud Developer
• Google Professional Cloud Architect (deseable para perfil SR)
• Google Professional Machine Learning Engineer
• Google Associate Cloud Engineer

Amazon Web Services (AWS)

• AWS Certified Developer – Associate
• AWS Certified Solutions Architect – Associate
• AWS Certified Machine Learning – Specialty (altamente alineada a IA/ML)
• AWS DevOps Engineer – Professional (deseable para perfil SR)

Microsoft Azure

• Microsoft Certified: Azure Developer Associate (AZ‑204)
• Microsoft Certified: Azure AI Engineer Associate
• Microsoft Certified: Azure DevOps Engineer Expert

Machine Learning

• Google Professional Machine Learning Engineer
• AWS Certified Machine Learning – Specialty
• Azure Data Scientist Associate
• TensorFlow Developer Certificate
• IBM AI Engineering Professional Certificate

Idioma

• Inglés técnico (deseable)

Habilidades Blandas

• Trabajo en equipo
• Autogestión
• Perfil autodidacta
• Comunicación efectiva
• Resolución de problemas
• Adaptabilidad
• Proactividad
• Pensamiento analítico

Requisitos Técnicos

Versionador e Integrador de Código

• Git
• GitHub Actions / GitLab CI
• Cloud Build / Cloud Deploy (GCP)
• CodeBuild / CodePipeline (AWS)
• Azure DevOps Pipelines

Lenguajes de Programación

• Python
• Node.js
• TypeScript (deseable)
• SQL
• YAML (pipelines / Infraestructura como Código)

Sistemas Operativos

• Linux (principal)
• Windows
• macOS

Manejadores de Bases de Datos

• Cloud SQL
• Amazon RDS
• Azure SQL
• BigQuery
• Bases de datos relacionales y analíticas
• Almacenamiento de objetos:
• Amazon S3
• Google Cloud Storage
• Azure Blob Storage

Frameworks de Desarrollo

Backend

• Express
• Fastify (Node.js)

IA / ML

• scikit-learn
• TensorFlow / Keras
• PyTorch

NLP

• spaCy
• NLTK
• Hugging Face Transformers (deseable)

IDE

• Visual Studio Code
• IDEs compatibles con Python y Node.js

Herramientas

• Docker
• Kubernetes (GKE / EKS / AKS)
• Helm
• Terraform
• MLflow / DVC
• Postman (pruebas de APIs)
• Prometheus / Grafana
• OpenTelemetry

Plataformas Cloud

• Google Cloud Platform (principal)
• Amazon Web Services (deseable)
• Microsoft Azure (deseable)

Asistentes de IA

• No especificados explícitamente
• (Uso implícito de frameworks y plataformas de IA/ML)

Conocimiento del Negocio

Marcos o Metodologías de Trabajo

• Metodología Ágil
• Scrum

Herramientas para Seguimiento y Control de Proyectos

• Gestión de backlog (herramientas ágiles no especificadas)
• Métricas ágiles:
• Velocity
• Lead Time
• Tableros de observabilidad
• Definición y seguimiento de SLO / SLI

Esquema de Trabajo

• Asignación a proyectos
• Desarrollo y operación de productos digitales
• (No esquema de fábrica de software)

Tipo de Asignación

• Proyectos
• Operación
• Soporte productivo
• Mejora continua

Tipo de Proyecto

• Ágil

Industria

• Banca / Servicios Financieros

Tecnología

• Cloud Computing
• Inteligencia Artificial / Data

Note: This isn’t an active role right now, but we’re building a community of great talent for future opportunities at Caylent. If your background aligns with what we’re looking for, our team may reach out to learn more about you and explore potential future fits.

The Mission

At Caylent, a Senior Machine Learning Engineer works as an integral part of a cross-functional delivery team to design and document machine learning solutions on the AWS cloud for our customers.  We are looking for someone that has a strong understanding of the various model types and tools, and can help our customers connect their business goals with the details of feature design, model training and inference.  You will develop solutions designed by an architect.

You will participate in daily standup meetings with your team and bi-weekly agile ceremonies with the customer.  Your manager will have a weekly 1:1 with you to help guide you in your career and make the most of your time at Caylent.

Your Assignments

• Work with a team to deliver machine learning solutions on AWS for customers
• Participate in and contribute to daily standup meetings
• Develop and implement ML models, MLOps, and analytics
• Big data processing and preparation of training data for models

Your Qualifications

• Strong experience in building ML models for real world applications
• Strong experience in at least one of these:
• AWS ML Services/SageMaker
• ML libraries like Keras, Tensorflow, PyTorch, Scikit-learn
• MLOps tools such as MLflow, Kubeflow, Airflow
• Advanced analytics using time series forecasting and/or inferential statistics
• Strong experience in one or more of these data processing solutions:
• Big data processing platforms like Spark, Hadoop, or streaming platforms
• Data processing and cleansing using Python/Pandas, PySpark, Scala, SQL
• Strong understanding of feature definition, model meta-data, hyperparameter tuning, stochastic gradient descent, deep learning layer types and activation functions
• Experience in visualization using SageMaker, ggplot, matplotlib, or seaborn
• Experience with an IaC tool such as CloudFormation, Amazon CDK or Terraform
• Excellent written and verbal communication skills

Benefits 

• 100% remote work
• Medical Insurance for you and eligible dependents
• Generous holidays and flexible PTO
• Competitive phantom equity
• Paid for exams and certifications
• Peer bonus awards
• State of the art laptop and tools
• Equipment & Office Stipend
• Individual professional development plan
• Annual stipend for Learning and Development
• Work with an amazing worldwide team and in an incredible corporate culture

This role may require up to 25% travel, depending on business needs.

The Opportunity

This is a senior technical client leadership role that blends deep hands-on ML expertise with strategic advisory and consulting skills. You will be the most experienced ML voice across a diverse and expanding book of customer engagements — from early-stage companies bringing ambitious ML ideas to market, to established enterprises modernizing how they build and operate AI systems on AWS.

You will shape strategy, influence architecture, and leave every team you touch better than you found it. You bring the scientific depth to design and evaluate models rigorously, the engineering depth to architect production ML systems at scale, and the consulting instincts to translate both into business value for customers.

If you have led the hard conversations, shaped the architecture decisions that mattered, and built the things others benchmark against — and you are looking to do that across a growing portfolio of varied and interesting customers — this is the role for you.

What You'll Do

• Lead end-to-end ML assessments across infrastructure, data pipelines, model lifecycle, and organizational readiness — producing recommendations that drive executive decision-making and earn Caylent the next engagement.
• Partner with sales and solutions teams through the proposal and scoping phase, contributing the technical depth needed to shape well-grounded statements of work.
• Serve as the senior technical authority on client engagements — possibly across multiple projects simultaneously — providing architectural guidance, ensuring technical quality from your project team members, and getting hands-on when the engagement demands it, without owning day-to-day implementation responsibilities.
• Own or orchestrate high-quality POCs that give customers confidence before committing to a larger initiative.
• Advise customers on ML operations standards and architecture — covering MLOps pipeline design, model lifecycle management, LLMOps patterns, and production monitoring frameworks — translating operational complexity into decisions and guardrails their teams can own and sustain.
• Shape how Caylent wins its most technically complex opportunities — contributing the architectural thinking and credibility that turns prospects into customers.
• Strengthen the ML practice from the inside — through peer guidance, technical interviews, and contributions to accelerators, reference architectures, and thought leadership content.

What You Bring

The non-negotiables

• 10+ years in machine learning or AI, with a proven track record of leading client-facing engagements in a consulting or advisory capacity.
• Deep, current knowledge of the AWS ML and GenAI ecosystem, with the ability to make and defend architectural decisions across the full ML lifecycle — from data and feature engineering through training, deployment, and monitoring.
• Deep expertise in at least two or three ML domains — whether traditional ML, computer vision, NLP, time series, or others — combined with the judgment to assess, architect, and advise across the broader ML landscape.
• Proven ability to architect and govern production ML systems end-to-end, translating MLOps, LLMOps, and broader AI operations complexity into standards and decisions that engineering teams can execute and executives can act on.
• Deep expertise across foundation model adaptation — fine-tuning (LoRA, QLoRA, PEFT), alignment (RLHF, DPO), inference optimization (quantization, vLLM), and distributed training (DeepSpeed, FSDP) — combined with RAG and agentic system design, including multi-agent architectures, event-driven workflows, MCP integration, and human-in-the-loop patterns on AWS. Technical authority to prescribe the right approach and set architectural standards that teams can execute against.
• Proven ability to operate independently in complex customer environments — navigating ambiguity, aligning stakeholders, and translating ML tradeoffs into business risk and value for both technical and executive audiences.

Strong differentiators

• AWS Certified Machine Learning – Specialty and/or AWS Certified Solutions Architect – Professional.
• Experience shaping practice-level standards, reference architectures, and reusable ML accelerators across multiple engagements.
• Exposure to varied industries and problem types in a consulting or client-facing context.
• Deep fluency in responsible AI practices — model evaluation, bias detection, fairness frameworks, and AI governance — applied in enterprise deployments.
• Hands-on experience designing and deploying SRE agents and AI-driven operations workflows in production — spanning automated incident detection, triage, and remediation — with the ability to integrate across observability platforms and translate AI operations outcomes into measurable business value.

Technical Stack

Our practice spans a broad range of ML domains. Candidates are expected to prescribe — not just recognize — with the judgment to maximize what AWS makes possible and the experience to know how open-source tooling strengthens it.

ML Domains: Classical ML, Computer Vision, NLP, Generative AI & LLMs, AI Agents & Autonomous Systems, Intelligent Document Processing, Video Understanding, Speech & Audio, Time Series & Forecasting, Recommender Systems, Graph ML, Reinforcement Learning, Multimodal AI

AWS ML Platform: SageMaker, SageMaker Pipelines, SageMaker Feature Store, SageMaker Model Registry, SageMaker Clarify, Bedrock (Agents, Knowledge Bases, Guardrails, AgentCore, Model Evaluation)

Multi-provider LLM: Bedrock, Anthropic API, OpenAI API, Google Gemini API, Azure OpenAI — with the judgment to reason across provider tradeoffs in enterprise contexts

AWS AI Services: Rekognition, Comprehend, Transcribe, Textract, Translate, Personalize, Neptune, Kinesis Video Streams, Polly

Data Platform: Apache Spark / PySpark, Apache Kafka, Amazon Kinesis, Apache Iceberg, Delta Lake, Apache Hudi, AWS Glue

Vector Databases: Pinecone, pgvector, Amazon OpenSearch (vector), Weaviate

Frameworks: PyTorch, TensorFlow, JAX, Scikit-learn, XGBoost, HuggingFace (Transformers, PEFT, TRL), LangChain, LlamaIndex, DSPy, Ollama

MLOps & Governance: MLflow, W&B, Airflow / MWAA (data orchestration), Dagster (asset-based pipelines), Kubeflow Pipelines, CI/CD, IaC (CloudFormation, CDK, Terraform), Docker, Kubernetes, ML Governance (lineage, data contracts, audit), Responsible AI / Bias & Fairness

LLM Evaluation & Safety: RAGAS, LLM-as-judge patterns, DeepEval, NeMo Guardrails, Constitutional AI patterns, structured output validation

Inference & Optimization: Triton, vLLM, SGLang, Trainium, Inferentia, Quantization (GPTQ, AWQ, bitsandbytes), SageMaker Neo

Benefits

The Mission

We are seeking an Engineering Manager for our Cloud Data Engineering Practice. The right candidate is someone who is technically strong, can manage a team of Architects and Engineers, oversee data delivery engagements and is passionate about working with our customers, partners and employees to drive the business forward.

Your Assignment

• Manage a team of 5 - 8 cloud engineers and architects
• Interface with clients and internal teams to propose technical improvements using your cloud and engineering knowledge
• Take on hands-on technical responsibilities, including technical tasks in sprints 
• Lead technical discussions with customers
• Handle code reviews and pull request management
• Manage task assignments within sprints
• Perform technical leadership activities like roadmap and vision checks, best practices implementation, and risk identification
• In-project mentoring (understand people on project & creation of growth opportunities) and sharing feedback for team members on the project
• Technical accountability on the delivery
• Kickoff new client engagements and act as a point of technical escalation
• Develop and review proposals and contracts and present these to clients
• Perform technical interviews for Architect and Engineer candidates
• Perform weekly 1:1s with your direct reports and guide their career development
•  

Your Qualifications

• Strong experience in AWS data landscape
• Expertise in at least two major areas of Cloud Data Engineering:
  • Relational database design, optimization and migration
  • Big data processing using Spark, Hadoop, EMR etc
  • Machine Learning and MLOps
  • Advanced analytics using time series forecasting, descriptive and inferential statistics
  • BI dashboards and visualizations
  • Data Governance and MDM
• Experience with most of these:
  • Infrastructure as Code tools
  • CI/CD pipelines
  • Application modernization and migration with a strong focus on databases
• Excellent written and verbal communication skills

Benefits

• 100% remote work
• Medical Insurance for you and eligible dependents
• Generous holidays and flexible PTO
• Competitive phantom equity
• Paid for exams and certifications
• Peer bonus awards
• State of the art laptop and tools
• Equipment & Office Stipend
• Individual professional development plan
• Annual stipend for Learning and Development
• Work with an amazing worldwide team and in an incredible corporate culture

Caylent is a place where everyone belongs.  We celebrate diversity and are committed to creating an inclusive environment for all employees. Our approach helps us to build a winning team that represents a variety of backgrounds, perspectives, and abilities. So, regardless of how your diversity expresses itself, you can find a home here at Caylent.   

We are proud to be an equal opportunity employer. We prohibit discrimination and harassment of any kind based on race, color, religion, national origin, sex (including pregnancy), sexual orientation, gender identity, gender expression, age, veteran status, genetic information, disability, or other applicable legally protected characteristics. If you would like to request an accommodation due to a disability, please contact us at hr@caylent.com.  

The Role

The Data Practice Director is a senior leadership role within Caylent's Cloud Data Engineering (CDE) practice, reporting directly to the Data Practice Leader. This person is responsible for operationalizing the practice strategy, leading a team of data professionals, and owning delivery excellence across a portfolio of client engagements. They are the connective tissue between strategic direction and day-to-day execution, ensuring the practice delivers consistent, high-quality outcomes while scaling to meet growth targets.

Your Assignment

Practice Operations and Delivery

• Own end-to-end delivery oversight for a portfolio of data engineering and analytics engagements, ensuring on-time, on-budget, and high-quality outcomes.
• Drive operational rigor across the practice including utilization management, staffing coordination, margin tracking, and engagement health reviews.
• Develop and enforce delivery standards, reusable frameworks, and best practices that enable consistent, scalable execution across the team.
• Serve as an escalation point for active engagements, helping resolve technical blockers, scope issues, and client concerns in partnership with delivery leads.
• Partner with the Practice Leader to translate the strategic roadmap into executable quarterly priorities and team-level OKRs.

Team Leadership and Development

• Directly lead and develop a team of data engineers, architects, and analytics professionals across the practice.
• Set clear performance expectations, conduct regular reviews, and provide ongoing coaching to build a high-performing, engaged team.
• Partner with recruiting and workforce planning to identify talent gaps, support hiring decisions, and onboard new team members effectively.
• Foster a culture of ownership, continuous learning, and technical excellence across a globally distributed team.
• Identify and develop emerging leaders within the practice, creating growth pathways aligned to practice needs.

Client Engagement

• Build trusted advisor relationships with key client stakeholders, acting as a senior point of contact for delivery and strategic guidance.
• Partner with the sales and GTM teams to support business development activities including scoping, proposal development, and technical solutioning for new and existing accounts.
• Identify and pursue expansion opportunities within active client engagements, surfacing new use cases and service offerings that align with client needs.
• Represent Caylent with credibility and professionalism in client-facing conversations at the director and VP level.

Practice Growth and Enablement

• Collaborate with the Practice Leader on go-to-market positioning, service offering development, and internal capability building.
• Contribute to thought leadership content including whitepapers, webinars, and conference presentations that elevate the practice's external profile.
• Stay current on trends in the modern data ecosystem and work with the team to incorporate relevant innovations into our delivery approach.
• Support the development of practice IP including accelerators, reference architectures, and reusable solution components.

Your Qualifications

Experience

• 12+ years of experience in professional services or consulting, with a strong focus on data engineering, analytics, or data platform delivery.
• Demonstrated success managing and growing a team of 20 or more data professionals in a services or consulting environment.
• Proven track record of delivering complex data projects on AWS, including data lake, lakehouse, streaming, and analytics workloads.
• Experience managing a portfolio of concurrent client engagements, balancing delivery quality with financial performance.
• Hands-on background in data architecture or engineering, with the ability to engage credibly with technical teams and client stakeholders alike.

Leadership

• Strong people management skills with demonstrated ability to develop talent, drive performance, and build team culture across geographies.
• Ability to operate effectively at multiple levels, from strategic planning conversations with the Practice Leader to hands-on engagement support with delivery teams.
• Comfortable navigating ambiguity and complexity in a fast-paced, high-growth consulting environment.
• Experience influencing senior client stakeholders (director, VP and C-level) in delivery and advisory contexts.

Technical Acumen

• Deep familiarity with the AWS data ecosystem including services such as Redshift, Glue, EMR, Kinesis, Lake Formation, and Athena.
• Working knowledge of adjacent platforms including Databricks and Snowflake, and their integration with AWS-native architectures.
• Broad understanding of data engineering fundamentals: ingestion patterns, transformation frameworks (e.g., Spark, dbt), data modeling, governance, and quality management.
• Familiarity with MLOps and data science workflows sufficient to support and position those capabilities in client conversations.

Education

• Bachelor's degree in Computer Science, Data Science, Information Systems, or a related field.
• Master's degree or MBA is a plus.

Certifications

• AWS Certified Data Analytics, AWS Certified Solutions Architect, or equivalent certifications are a plus.

Preferred Qualifications

• Execution-Oriented: Skilled at translating strategy into structured plans and holding teams accountable to outcomes.
• Client-First Mindset: Genuinely invested in client success and capable of building durable, trust-based relationships.
• Collaborative Partner: Works effectively across sales, delivery, and practice leadership functions without siloed thinking.
• Clear Communicator: Able to convey complex technical and business concepts to diverse audiences with clarity and confidence.
• Growth Driver: Passionate about building teams, capabilities, and client relationships that compound over time.

Benefits

• 100% remote work
• Medical Insurance for you and eligible dependents
• Generous holidays and flexible PTO
• Competitive phantom equity
• Paid for exams and certifications
• Peer bonus awards
• State of the art laptop and tools
• Equipment & Office Stipend
• Individual professional development plan
• Annual stipend for Learning and Development
• Work with an amazing worldwide team and in an incredible corporate culture