Marketing Automation Manager

Founding Engineer (Bangalore)

The problem:

Business enterprises overpay vendors - on every batch of invoices, on every month because the data that would catch lives in different systems. We are building an AI agent that processes invoices end-to-end, reasons across all the relevant sources, flags genuine discrepancies, and acts - without a human having to investigate each one.

What you will own

Everything engineering. Schema design to deployment to the 2am fix when something breaks in production. There is no tech lead above you. There is no platform team. There is the architecture, you, and the founders. Concretely, this means building:

• A multi-stage agentic pipeline that takes a vendor invoice and produces a structured decision - fully autonomous for clear cases, escalating to human review for genuinely ambiguous ones. We use LangGraph, but if you've built equivalent systems with Temporal, Prefect, or custom state machines with LLM orchestration, that works
• An LLM-powered extraction layer that handles real invoices - scanned PDFs, stamped documents, inconsistent layouts - and returns structured output
• A graph data model that connects invoices to various sources and can traverse those relationships to detect discrepancies
• ERP connectors, GST validation logic, and a write-back layer that closes the loop

What we need

• Strong Python. Async FastAPI, clean service boundaries, tests that actually catch bugs. You have shipped Python backends that handled real production load
• Solid Postgres. Complex queries, schema design, migrations without downtime, row-level security for multi-tenant data. pgvector is a plus - if not, you pick it up fast
• LLM API experience in production. You have called an LLM API for something that real users depended on. You know about structured output, retry logic, cost management, prompt versioning. A side project counts if it was genuinely deployed
• Comfort with graph data models. You understand when a graph is the right structure and when it is not. You do not need deep Neo4j production experience - you need to understand graph relationships conceptually and be willing to learn Cypher. It is a 2-day ramp for the right person
• Working knowledge of deployment. Deployed and operated production workloads on GCP. Cloud Run, Cloud SQL, Cloud Storage, Redis — you're comfortable across the stack. If you've done it on AWS, the translation isn't hard, but GCP is where we are
• You own things. Not "I contributed to" - you designed it, shipped it, and fixed it when it broke. That pattern needs to be visible in your history

Good to have, not mandatory

• Built an agentic pipeline with multiple stages
• Any fintech, P2P domain experience - even tangential
• Worked at a startup with under 20 people
• Has a GitHub, blog, or writeup that shows how you think about a hard technical problem

What you get

• The hardest engineering problem you would have worked on. This is not CRUD with an LLM bolted on
• Real ownership. First engineering hire. Your architectural decisions will be in this product five years from now
• Equity that matters. ESOP - Open to discussion. We are pre-seed - this is a bet, not a guarantee. We will not pretend otherwise
• No meetings tax. You work directly with the founders. The product is specified clearly. You know what you are building and why

Honest about stage: We do not have a production ready infra yet. We have a complete architecture specification and a working prototype. If you need the stability of an established engineering org, this is not the right moment. If you want to build something real from zero and own a meaningful piece of it, it is.

The founders

One of us has spent 20 years building revenue and operational engines at companies where there was no playbook - part of the pilot team that established the world's largest search company's direct sales operations in India, managed global operations for a global mobile advertising platform, scaled a B2C platform to become one of India’s leading edtech platforms and most recently worked on building an enterprise Agentic Voice AI platform. The other has spent 15 years taking AI from demo to production in domains where failure is expensive - voice, lending, and conversational systems across a Series D conversational AI company, a major telco, a Big 4, and a leading NBFC.

Two IIT/IIM alumni who have both watched AI work in enterprise, and know exactly what it takes to get it there. We are not building this product because it sounds interesting. We are building it because we have both sat across the table from CFOs who know they are losing margin and have no tool capable of doing anything about it.

Dear Candidate,

We are Urgently hiring QA Automation Engineers and Test leads At Hyderabad and Bangalore

Exp: 6-10 yrs

Locations: Hyderabad ,Bangalore

JD:

we are Hiring Automation Testers with 6-10 years of Automation testing experience using QA automation tools like Java, UFT, Selenium, API Testing, ETL & others

Must Haves:

·        Experience in Financial Domain is a must

·        Extensive Hands-on experience in Design, implement and maintain automation framework using Java, UFT, ETL, Selenium tools and automation concepts.

·        Experience with AWS concept and framework design/ testing.

·        Experience in Data Analysis, Data Validation, Data Cleansing, Data Verification and identifying data mismatch.

·        Experience with Databricks, Python, Spark, Hive, Airflow, etc.

·        Experience in validating and analyzing kubernetics log files.

·        API testing experience

·        Backend testing skills with ability to write SQL queries in Databricks and in Oracle databases

·        Experience in working with globally distributed Agile project teams

·        Ability to work in a fast-paced, globally structured and team-based environment, as well as independently

·        Experience in test management tools like Jira

·        Good written and verbal communication skills

Good To have:

• Business and finance knowledge desirable

Best Regards,

Minakshi Soni

Executive - Talent Acquisition (L2)

Worldwide Locations: USA | HK | IN 

Technical Experience:

-Translate designs and wireframes into high-quality code

-Design, build and maintain high performance, reusable, and reliable Java code

-Ensure the best possible performance, quality, and responsiveness of the application

-Identify and correct bottlenecks and fix bugs

-Help maintain code quality, organization, and automatization

-Strong knowledge of Android SDK, different versions of Android, and how to deal with different screen sizes

-Familiarity with RESTful APIs to connect Android applications to back-end services

-Strong knowledge of Android UI design principles, patterns, and best practices

-Experience with offline storage, threading, and performance tuning

-Ability to design applications around natural user interfaces, such as touch

-Familiarity with the use of additional sensors, such as gyroscopes and accelerometers

-Knowledge of the open-source Android ecosystem and the libraries available for common tasks

-Ability to understand business requirements and translate them into technical requirements

-Familiarity with cloud message APIs and push notifications

-A knack for benchmarking and optimization

-Understanding of Googles Android design principles and interface guidelines

-Proficient understanding of code versioning tools, such as Git

ABOUT EPISOURCE:

Episource has devoted more than a decade in building solutions for risk adjustment to measure healthcare outcomes. As one of the leading companies in healthcare, we have helped numerous clients optimize their medical records, data, analytics to enable better documentation of care for patients with chronic diseases.

The backbone of our consistent success has been our obsession with data and technology. At Episource, all of our strategic initiatives start with the question - how can data be “deployed”? Our analytics platforms and datalakes ingest huge quantities of data daily, to help our clients deliver services. We have also built our own machine learning and NLP platform to infuse added productivity and efficiency into our workflow. Combined, these build a foundation of tools and practices used by quantitative staff across the company.

What’s our poison you ask? We work with most of the popular frameworks and technologies like Spark, Airflow, Ansible, Terraform, Docker, ELK. For machine learning and NLP, we are big fans of keras, spacy, scikit-learn, pandas and numpy. AWS and serverless platforms help us stitch these together to stay ahead of the curve.

ABOUT THE ROLE:

We’re looking to hire someone to help scale Machine Learning and NLP efforts at Episource. You’ll work with the team that develops the models powering Episource’s product focused on NLP driven medical coding. Some of the problems include improving our ICD code recommendations, clinical named entity recognition, improving patient health, clinical suspecting and information extraction from clinical notes.

This is a role for highly technical data engineers who combine outstanding oral and written communication skills, and the ability to code up prototypes and productionalize using a large range of tools, algorithms, and languages. Most importantly they need to have the ability to autonomously plan and organize their work assignments based on high-level team goals.

You will be responsible for setting an agenda to develop and ship data-driven architectures that positively impact the business, working with partners across the company including operations and engineering. You will use research results to shape strategy for the company and help build a foundation of tools and practices used by quantitative staff across the company.

During the course of a typical day with our team, expect to work on one or more projects around the following;

1. Create and maintain optimal data pipeline architectures for ML

2. Develop a strong API ecosystem for ML pipelines

3. Building CI/CD pipelines for ML deployments using Github Actions, Travis, Terraform and Ansible

4. Responsible to design and develop distributed, high volume, high-velocity multi-threaded event processing systems

5. Knowledge of software engineering best practices across the development lifecycle, coding standards, code reviews, source management, build processes, testing, and operations  

6. Deploying data pipelines in production using Infrastructure-as-a-Code platforms

7. Designing scalable implementations of the models developed by our Data Science teams  

8. Big data and distributed ML with PySpark on AWS EMR, and more!

BASIC REQUIREMENTS 

1.  Bachelor’s degree or greater in Computer Science, IT or related fields
   
   

2.  Minimum of 5 years of experience in cloud, DevOps, MLOps & data projects
   
   

3. Strong experience with bash scripting, unix environments and building scalable/distributed systems
   
   

4. Experience with automation/configuration management using Ansible, Terraform, or equivalent
   
   

5. Very strong experience with AWS and Python
   
   

6. Experience building CI/CD systems
   
   

7. Experience with containerization technologies like Docker, Kubernetes, ECS, EKS or equivalent

8. Ability to build and manage application and performance monitoring processes

• Act as a key point of contact to the Business Transformation Services, Business Analysis, and Functional Architecture and Solution Architecture groups throughout the implementation
• Interface with business stakeholders to determine key design/process decisions, and contribute to overall delivery plan.
• Deliver assigned tasks complete and on time as designated by the Programme Office to ensure smooth progress of the implementation.
• Contribute and take ownership for various project related tasks / planning activities as the programme evolves

• Knowledge and Practical experience of Project Management methodologies – ability to configure delivery work stream frameworks to progress timelines and delivery milestones
• Demonstrate ability in planning consulting/functional work & ownership of project outcomes