Hashone Careers

What we are looking for

 3–8 years of experience in consumer insights, market research, strategy consulting, social intelligence, cultural insights, or a related research environment.

 Experience independently owning custom research projects—from ambiguous brief through analysis,

synthesis, recommendation, and client presentation.

 Strong qualitative and quantitative reasoning. You can work with both nuance and numbers without

overstating what the data proves.

 Exceptional PowerPoint or Google Slides craft: storyline, executive writing, information hierarchy, charts, visual examples, and disciplined editing.

 A portfolio of client-ready research decks or case studies that demonstrates synthesis and

storytelling—not merely attractive slide design.

 Confident client presence and clear spoken communication, including experience presenting to senior

stakeholders and navigating difficult questions.

 Hands-on fluency with modern AI tools for research and creative production. You know how to

prompt, critique, verify, refine, and combine outputs into work that does not feel machine-generated.

 High ownership, speed, curiosity, and composure in a startup where priorities can move quickly and

important projects can arrive with short timelines.

You will stand out if you have

 Experience in beauty, personal care, food and beverage, nutrition, home care, or another consumer category.

 Agency-side experience managing multiple clients and deadlines without sacrificing quality.

 Experience with social/video intelligence, digital ethnography, online communities, search, reviews, or

creator/influencer data.

 A strong visual instinct and comfort using image-generation or design-assistance tools to make

abstract insights tangible.

 Experience leading or mentoring researchers, even if you have not formally managed a large team.

Key Responsibilities

• Design, build, and deploy machine learning and AI models that power Transient.AI's core products (research

automation, document intelligence, investor matching, and workflow orchestration).

• Work on applied NLP/LLM systems, including retrieval-augmented generation, structured extraction from

unstructured financial documents, and model evaluation pipelines.

• Partner closely with product and founding engineers to translate capital markets workflows into scalable AI

systems.

• Own model performance, reliability, and cost — from experimentation through production deployment.

• Build and maintain data pipelines, feature stores, and evaluation frameworks to support rapid iteration.

• Ensure systems meet the compliance, auditability, and security standards required in regulated financial

environments.

What We're Looking For

• 5+ years of experience building and deploying machine learning or AI systems in production.• Strong hands-on experience with Python and modern ML/AI frameworks (PyTorch, TensorFlow, Hugging Face,

LangChain, or equivalent).

• Experience with LLMs — fine-tuning, prompt engineering, RAG architectures, or agentic systems — is highly

valued.

• Solid grounding in data structures, distributed systems, and MLOps practices (model serving, monitoring,

versioning).

• Prior experience at a strong product company, high-growth startup, or a top-tier engineering background

• Comfort operating in an early-stage, high-ownership environment with limited process and high ambiguity.

• Exposure to fintech, capital markets, or other regulated industries is a plus, though not mandatory

Must-have

• LLB or LLM from a recognised institution

• 2–4 years of hands-on corporate / transactional legal experience

• Prior experience in a startup, VC-backed company, or fast-paced legal setup (not just largelaw firms)

• Proven ability to draft and redline contracts independently

• MCA / ROC filings done without supervision

• Strong written English — your documents represent Convosight externally

• Highly organised — zero misses on deadlines and compliance calendars

• Ability to work across teams without needing hand-holding

Good to have

• Experience supporting a fundraise round (Series A / B due diligence preferred)

• Working knowledge of DPDP Act, GDPR basics, or data privacy frameworks

• Exposure to global client contracts (US / EU counterparties) • Basic familiarity with SEBI / FEMA regulations

• Comfortable with DocuSign, contract lifecycle tools, or Notion / Jira for task tracking

About LodgIQ

Headquartered in New York, LodgIQ delivers a revolutionary B2B SaaS platform to the travel industry. By leveraging machine learning and artificial intelligence, we enable precise forecasting and optimized pricing for hotel revenue management. Backed by Highgate Ventures and Trilantic Capital Partners, LodgIQ is a well-funded, high-growth startup with a global presence.

Role Summary:

We are seeking a Senior DevOps Engineer with 5+ years of strong hands-on experience in AWS, Kubernetes, CI/CD, infrastructure as code, and cloud-native technologies. This role involves designing and implementing scalable infrastructure, improving system reliability, and driving automation across our cloud ecosystem.

Key Responsibilities:

• Architect, implement, and manage scalable, secure, and resilient cloud

infrastructure on AWS

• Lead DevOps initiatives including CI/CD pipelines, infrastructure automation, and monitoring

• Deploy and manage Kubernetes clusters and containerized microservices

• Define and implement infrastructure as code using Terraform/CloudFormation

• Monitor production and staging environments using tools like CloudWatch, Prometheus, and Grafana

• Support MongoDB and MySQL database administration and optimization

• Ensure high availability, performance tuning, and cost optimization

• Guide and mentor junior engineers, and enforce DevOps best practices

• Drive system security, compliance, and audit readiness in cloud environments

• Collaborate with engineering, product, and QA teams to streamline release processes

Required Qualifications:

• 5+ years of DevOps/Infrastructure experience in production-grade environments

• Strong expertise in AWS services: EC2, EKS, IAM, S3, RDS, Lambda, VPC, etc.

• Proven experience with Kubernetes and Docker in production

• Proficient with Terraform, CloudFormation, or similar IaC tools

• Hands-on experience with CI/CD pipelines using Jenkins, GitHub Actions, or similar

• Advanced scripting in Python, Bash, or Go

• Solid understanding of networking, firewalls, DNS, and security protocols

• Exposure to monitoring and logging stacks (e.g., ELK, Prometheus, Grafana)

• Experience with MongoDB and MySQL in cloud environments

Preferred Qualifications:

• AWS Certified DevOps Engineer or Solutions Architect

• Experience with service mesh (Istio, Linkerd), Helm, or ArgoCD

• Familiarity with Zero Downtime Deployments, Canary Releases, and Blue/Green Deployments

• Background in high-availability systems and incident response

• Prior experience in a SaaS, ML, or hospitality-tech environment

Tools and Technologies You’ll Use:

• Cloud: AWS

• Containers: Docker, Kubernetes, Helm

• CI/CD: Jenkins, GitHub Actions

• IaC: Terraform, CloudFormation

• Monitoring: Prometheus, Grafana, CloudWatch

• Databases: MongoDB, MySQL

• Scripting: Bash, Python

• Collaboration: Git, Jira, Confluence, Slack

Why Join Us?

• Competitive salary and performance bonuses.

• Remote-friendly work culture.

• Opportunity to work on cutting-edge tech in AI and ML.

• Collaborative, high-growth startup environment.

• For more information, visit http://www.lodgiq.com

About the job

Must-Have Skills

• 5+ years in data architecture / data engineering, with at least 2+ years in an architect or lead capacity
• Strong SQL — advanced query optimization, indexing, partitioning strategies
• Data modeling — dimensional modeling (star/snowflake schema), normalization/denormalization tradeoffs, entity relationship design
• Cloud data platforms — hands-on with AWS (Redshift, S3, Glue), Azure (Synapse, Data Factory), or GCP (BigQuery, Dataflow)
• Big data ecosystems — Spark, Hadoop, or Kafka for large-scale/streaming data
• Data warehousing — Snowflake, Redshift, BigQuery, or Databricks
• ETL/ELT pipeline design — Airflow, dbt, Fivetran, or similar orchestration tools
• Data governance & security — data lineage, access control, compliance (GDPR/SOC2), master data management

Strongly Preferred

• Experience architecting systems supporting ML/AI pipelines (feature stores, vector DBs, real-time inference data flows)
• Programming — Python or Scala for pipeline development
• API/microservices architecture exposure — understanding how data systems integrate with application layers
• Experience with data mesh / data lake house architectures
• Prior experience presenting architecture decisions to leadership/stakeholders

Key Responsibilities

• Own and drive the product roadmap for data and AI-powered features end-to-end

• Translate complex data and AI capabilities into simple, intuitive product experiences for brands and creators

• Collaborate closely with data engineers, ML engineers, and business teams to define and deliver product requirements

• Define product metrics and success criteria — including data quality, model accuracy, and user engagement

• Identify gaps in existing data pipelines and work with engineering to build scalable solutions

• Conduct user research, competitor analysis, and market mapping to inform product decisions

• Prioritise features and manage the product backlog with a strong data-driven approach

• Work with the AI team to evaluate and integrate LLM and GenAI capabilities into the product

Must-Have Skills

• Total experience of 2–3 years with at least 1–2 years in data engineering or data analytics

• Transitioned into or actively pursuing a product management role

• Strong understanding of data pipelines, ETL processes, SQL, and data modelling

• Ability to write and interpret data queries to inform product decisions

• Excellent communication and stakeholder management skills

• Strong product thinking — ability to break down complex problems into simple product solutions

Key Responsibilities

• Design, build, and deploy machine learning and AI models that power Transient.AI's core products (research automation, document intelligence, investor matching, and workflow orchestration).

• Work on applied NLP/LLM systems, including retrieval-augmented generation, structured extraction from unstructured financial documents, and model evaluation pipelines.

• Partner closely with product and founding engineers to translate capital markets workflows into scalable AI systems.

• Own model performance, reliability, and cost — from experimentation through production deployment.

• Build and maintain data pipelines, feature stores, and evaluation frameworks to support rapid iteration.

• Ensure systems meet the compliance, auditability, and security standards required in regulated financial environments.

What We're Looking For

• 5+ years of experience building and deploying machine learning or AI systems in production.

• Strong hands-on experience with Python and modern ML/AI frameworks (PyTorch, TensorFlow, Hugging Face, LangChain, or equivalent).

• Experience with LLMs — fine-tuning, prompt engineering, RAG architectures, or agentic systems — is highly valued.

• Solid grounding in data structures, distributed systems, and MLOps practices (model serving, monitoring, versioning).

• Prior experience at a strong product company, high-growth startup, or a top-tier engineering background

• Comfort operating in an early-stage, high-ownership environment with limited process and high ambiguity.

Exposure to fintech, capital markets, or other regulated industries is a plus, though not mandatory

What We're Looking For

MUST-HAVE

• Proven track record shipping AI/ML products — LLMs, agentic systems, NLP, or vision

• Deep understanding of transformers, embeddings, fine-tuning, and RAG — not surface-level

• GTM experience: positioning, pricing, channel strategy, and adoption metrics

• Working knowledge of at least one programming language (Python preferred)

• Ability to turn ambiguous problems into product decisions quickly

GOOD TO HAVE

• Hands-on with agentic AI frameworks (LangChain, AutoGen, CrewAI, or similar)

• Background in consumer research, CPG/FMCG, or social analytics

• Experience in a zero-to-one or founding team environment

• Engineering or technical degree from a Tier 1 institution

Why Join

• Work with a CPTO who codes — no layers between your ideas and engineering

• Real clients, real scale — your product drives billion-dollar decisions at Unilever, P&G;

• Category-defining space — no dominant player in video-native consumer intelligence yet

• Small team (~132 people), startup pace, enterprise credibility

• Ownership and impact from Day 1

Who We Are

We’re a small, global team shipping ambitious features at a pace that would overwhelm most.

The kind of pace where QA isn’t a back office function — it’s the heartbeat of everything we

ship. Our customers expect speed and trust. QA is where those two meet.

What We Need

We’re looking for a QA Specialist who thrives in fast-moving environments. Someone who can:

● Spot gaps before they become failures.

● Shift gears as priorities change — without losing accuracy.

● Push testing forward with the urgency of a live launch.

This isn’t about ticking boxes on a test plan. It’s about owning the quality of what reaches the

customer, no matter how quickly the roadmap evolves.

Your Craft

● 3–5 years of experience in software QA (manual, automated, or both).

● Fast learner who can get inside the product’s mind and test like a real user.

● Ability to design and execute test cases at lightning speed — without compromising

depth.

● Comfort with dynamic priorities: you can validate a demo at noon and regression test a

release by 6pm.

● Familiarity with QA tools, automation frameworks, and bug tracking — but more

importantly, the instinct to know what needs to be tested first.

Key Responsibilities

Backend Development

• Design, develop, and maintain scalable backend services using Rust, Python, and TypeScript.
• Build high-performance microservices and APIs for AI-driven products.
• Develop fault-tolerant, secure, and maintainable distributed systems.
• Design event-driven architectures and asynchronous processing pipelines.
• Implement caching, message queues, and database optimization strategies.

AI & Machine Learning Integration

• Build and maintain backend infrastructure supporting LLMs, Generative AI, RAG systems, and AI Agents.
• Integrate models from providers such as OpenAI, Anthropic, Gemini, and open-source LLMs.
• Develop vector search solutions using Pinecone, Weaviate, Qdrant, or similar technologies.
• Build AI workflows for content generation, summarization, sentiment analysis, and conversational intelligence.
• Optimize AI inference pipelines for performance and cost efficiency.

System Architecture

• Design scalable architectures capable of handling high traffic and large volumes of data.
• Develop real-time processing systems and data pipelines.
• Implement observability, monitoring, logging, and alerting mechanisms.
• Drive performance tuning and latency optimization across services.

Cloud & DevOps

• Deploy and manage applications on AWS, GCP, or Azure.
• Work with Docker, Kubernetes, CI/CD pipelines, and Infrastructure as Code.
• Ensure system reliability, security, and scalability.