AI/ML Engineer
Build the data pipelines, LLM evaluations, and scoring systems that turn public business activity into relevant, explainable lead recommendations.
Learn more about us.
Cnvrted helps B2B revenue teams find companies with real buying intent. We bring together signals from social platforms, communities, and company websites, then use AI to evaluate ICP fit, intent, and timing. The goal is to help teams understand what changed, why it matters, and when to start a conversation.
We’re a small team from Bangalore, building in public, shipping fast, and talking to salespeople, founders, and GTM teams. Our question is simple: why buy another list of names when what you need is timing?
About the AI/ML Engineer role.
Build the intelligence behind Cnvrted’s lead discovery and ranking. You’ll turn public business activity into structured signals, match companies to an ideal customer profile, and make lead scores reliable and explainable.
How you will spend your time.
From raw activity to reliable signals
Work on extraction, enrichment, deduplication, and entity resolution across social platforms, communities, and company websites. Keep the source and timing of each signal clear so downstream decisions have evidence behind them.
LLM scoring & evaluation
Use language models to assess ICP fit, buying intent, and timing. Build evaluations that expose false positives and inconsistent rankings, and improve quality without losing sight of latency and inference cost.
Your responsibilities.
- Build pipelines that turn unstructured company and contact information into validated, structured records.
- Develop entity matching and deduplication logic so signals from different sources resolve to the right company or person.
- Implement and improve LLM-based extraction, classification, and lead scoring using clear schemas and source evidence.
- Create representative evaluation datasets and measure extraction accuracy, ranking quality, and failure patterns.
- Design retries, fallbacks, monitoring, and data-quality checks for model calls and processing jobs.
- Work with product and GTM teams to translate customer feedback into better relevance, score explanations, and account recommendations.
- Document experiments and trade-offs, and monitor model quality, latency, and cost after changes ship.
What we seek in a candidate.
- Strong Python and SQL skills, with experience building data-processing services or pipelines.
- Practical experience integrating LLM APIs, validating structured outputs, and evaluating their behavior on real examples.
- An understanding of classification, ranking, and common evaluation metrics, including how to investigate false positives.
- Comfort working with messy, incomplete, or conflicting data and designing checks that make its limitations visible.
- Sound software engineering habits: version control, tests, debugging, and observability.
- Ability to explain technical trade-offs clearly and work with product and customer-facing teammates.
