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Requirements:
Core Responsibilities
Design and implement end-to-end data digestion pipelines for complex unstructured and semi-structured inputs.
Integrate classical algorithms with LLM-centric workflows to produce high-quality contextual inputs for reasoning tasks.
Build systems for segmentation, structure extraction, semantic decomposition, embedding-based representations, and graph construction.
Develop agentic workflows that combine tools, retrieval, and reasoning for interactive customer experiences.
Design evaluation suites, datasets, metrics, and regression tests to measure quality, robustness, and performance over time.
Continuously refine workflows based on customer usage, failure analysis, and measurable improvements.
What We're Looking For
Required Qualifications
MSc (or equivalent experience) in Computer Science, Mathematics, Electrical Engineering, or a related quantitative field.
5+ years of experience in data science / algorithm development / applied research engineering roles.
Strong experience building complex algorithmic workflows beyond model calls - including multi-stage pipelines, feature extraction, structure inference, and optimization.
Deep understanding of LLM digestion concepts: RAG, context engineering, chunking strategies, retrieval and ranking, tool usage patterns, and reliability techniques.
Experience designing and maintaining evaluation frameworks for both classical algorithms and LLM workflows (offline + online, regression, benchmarking).
Strong programming skills in Python; familiarity with TypeScript is a plus.
Ability to thrive in a high-ownership startup environment: ambiguity, speed, responsibility, and continuous learning.
Nice To Have
Familiarity with vector databases and scalable retrieval architectures.
Familiarity with agent orchestration frameworks (e.g., LangChain) and tool-driven reasoning systems.
Experience deploying AI workflows into production with monitoring, guardrails, and iteration loops.

Requirements:
M.Sc. or higher in Computer Science, Mathematics, Engineering, or a related quantitative field.
7+ years of hands-on experience building and deploying ML/AI solutions in production, including 2+ years leading or growing a data science/ML team.
Proven experience designing and shipping GenAI-based product features and multi-agent systems using modern orchestration frameworks such as LangGraph, CrewAI, or AutoGen, including RAG, vector databases, and tool/function calling.
Practical experience with the agentic production stack: observability/eval tooling (e.g., Langfuse), durable workflow orchestration, and typed service design.
Strong foundation in classical machine learning - supervised/unsupervised methods, feature engineering, model evaluation - and sound judgment for when to use classical models over LLMs.
Advantage: Demonstrated experience fine-tuning and deploying small/large language models, including SFT and preference/RL-based methods such as GRPO or DPO.
Advantage: Experience with model optimization and efficient serving: quantization, pruning, LoRA/QLoRA, and high-throughput inference frameworks such as vLLM.
Advantage: Experience designing end-to-end ML pipelines (versioning, training/tuning, deployment, testing, monitoring) on cloud-native infrastructure (Kubernetes, GCP/AWS).

Requirements:
What Were Looking For
2-5 years of experience in one or more of the following:
Product / Business / Financial Analyst
Technical Product Support
Solutions Consultant
Product / FinTech Operations
Implementation Consultant
Experience working with SaaS or enterprise software.
Strong analytical skills and confidence working with numbers, financial data, and business metrics.
Excellent English (written and spoken) - mandatory.
Ability to understand complex business workflows and translate them into scalable product solutions.
Experience identifying product issues and recommending improvements.
Comfortable performing product validation and investigating functional issues.
Excellent communication skills and ability to collaborate across Product, Engineering, AI, and Customer Success teams.
High attention to detail and strong ownership.
Nice to Have
Experience in FinTech, Payments, Banking, Financial Services, ERP, or Enterprise SaaS.
Experience with AI products or LLM-based platforms.
Experience working with enterprise customers.
Experience using SQL, Excel, Google Sheets, or BI tools.
Background in accounting, finance, or business analysis.

Requirements:
For this mission, youll need:
3+ years of experience as a Data Analyst working with financial, banking, fintech, payments, or similar datasets.
Strong SQL skills and experience analyzing large, complex datasets.
Solid understanding of financial concepts, including transactions, accounts, reconciliation, and key metrics.
Experience with Python (Pandas) and BI/data visualization tools.
Strong analytical, problem-solving, and communication skills, with the ability to leverage AI tools effectively and validate results.

Nice to Have:
Experience with AML, fraud, risk, financial crime, or reconciliation analytics.
Familiarity with OSINT and third-party data sources.
Experience with technologies such as ClickHouse, Trino, Kafka, Spark, or Iceberg.
Experience with Apache Superset or similar BI platforms.
Knowledge of data warehouses, ETL processes, and Git.