Data Science UA

About us:
Data Science UA is a service company with strong data science and AI expertise. Our journey began in 2016 with uniting top AI talents and organizing the first Data Science tech conference in Kyiv. Over the past 9 years, we have diligently fostered one of the largest Data Science & AI communities in Europe.

About the client:
Our client's mission is to reduce the time and money enterprises spend on state-of-the-art technology by 99%. It is the core constraint that drives the product philosophy. It forces the company to reject incremental improvements and pursue fundamental breakthroughs. These principles are the operating system for how the company thinks, builds, and ships. They are how the company turns radical ambition into reality.

About the role:
We are looking for a Full Stack AI Engineer who will join the team. You'll be an architect of the revolution against enterprise tech waste. You'll build the core platform and intelligent agents that power the company's mission — not optimizing existing workflows, but eliminating them entirely.

Requirements:
- 5+ years of industry experience in product-centric full-stack engineering roles;
- Very strong software engineering background with deep expertise in Python/Typescript.
- Hands-on LLM product patterns (prompting, tool use, routing, evals, regression testing);
Experience of working with Agents & LLMs:
- LangGraph/LangChain (know its limitations & trade-offs);
- Multi-provider routing, prompt tooling, trace/eval discipline (LangSmith);

Experience of working with Knowledge & Search Technologies:
- Vector — pgvector on PostgreSQL (semantic);
- Graph traversal — Cypher on Neo4j (relationships & enterprise context graph);
- Keyword/BM25 — lexical precision;
- Cross-Encoder (BGE Reranker) + RRF (blend lists robustly);
Deep knowledge of working with Data & Databases:
- PostgreSQL (Cloud SQL) — primary OLTP;
- SQLAlchemy and BigQuery — analytics/outcomes & cost/waste telemetry;
- Redis (Memorystore) — caching + lightweight queue/broker;
- GCS — object storage (files, audio, artifacts);

Responsibilities:
Design & Build Autonomous Agents:
- Tackle the end-to-end challenge of creating agents that solve open-ended enterprise problems.
- Wrangling high-volume enterprise data, building robust evaluation frameworks.
- Mastering the interplay between LLM prompting, fine-tuning, and scalable system architecture.
- Lead Customer Conversations & Translate to Product: Work directly with customer executives to translate their most critical objectives into breakthrough product features.
- Build the "New": Own the most audacious hypotheses, like fully autonomous optimization agents that replace entire consulting engagements and obsolete the need for army-style deployments.
- Master Deep Context: Build systems that understand enterprise architecture patterns better than most architects, automatically discovering waste and optimization opportunities.
- Take single-threaded ownership of core AI product areas, with end-to-end responsibility for customer success and business outcomes.
- Double as a Forward Deployed Engineer: Work regularly onsite with customers, and actually work with the systems.
- Every customer engagement becomes R&D for the platform, turning individual pain points into generalizable breakthroughs that serve the entire customer base

About us:
Data Science UA is a service company with strong data science and AI expertise. Our journey began in 2016 with uniting top AI talents and organizing the first Data Science tech conference in Kyiv. Over the past 9 years, we have diligently fostered one of the largest Data Science & AI communities in Europe.

About the client:
Our client's mission is to reduce the time and money enterprises spend on state-of-the-art technology by 99%. It is the core constraint that drives the product philosophy. It forces the company to reject incremental improvements and pursue fundamental breakthroughs. These principles are the operating system for how the company thinks, builds, and ships. They are how the company turns radical ambition into reality.

About the role:
We are looking for a Full Stack AI Engineer who will join the team. You'll be an architect of the revolution against enterprise tech waste. You'll build the core platform and intelligent agents that power the company's mission — not optimizing existing workflows, but eliminating them entirely.

Requirements:
- 5+ years of industry experience in product-centric full-stack engineering roles;
- Very strong software engineering background with deep expertise in Python/Typescript.
- Hands-on LLM product patterns (prompting, tool use, routing, evals, regression testing);
Experience of working with Agents & LLMs:
- LangGraph/LangChain (know its limitations & trade-offs);
- Multi-provider routing, prompt tooling, trace/eval discipline (LangSmith);

Experience of working with Knowledge & Search Technologies:
- Vector — pgvector on PostgreSQL (semantic);
- Graph traversal — Cypher on Neo4j (relationships & enterprise context graph);
- Keyword/BM25 — lexical precision;
- Cross-Encoder (BGE Reranker) + RRF (blend lists robustly);
Deep knowledge of working with Data & Databases:
- PostgreSQL (Cloud SQL) — primary OLTP;
- SQLAlchemy and BigQuery — analytics/outcomes & cost/waste telemetry;
- Redis (Memorystore) — caching + lightweight queue/broker;
- GCS — object storage (files, audio, artifacts);

Responsibilities:
Design & Build Autonomous Agents:
- Tackle the end-to-end challenge of creating agents that solve open-ended enterprise problems.
- Wrangling high-volume enterprise data, building robust evaluation frameworks.
- Mastering the interplay between LLM prompting, fine-tuning, and scalable system architecture.
- Lead Customer Conversations & Translate to Product: Work directly with customer executives to translate their most critical objectives into breakthrough product features.
- Build the "New": Own the most audacious hypotheses, like fully autonomous optimization agents that replace entire consulting engagements and obsolete the need for army-style deployments.
- Master Deep Context: Build systems that understand enterprise architecture patterns better than most architects, automatically discovering waste and optimization opportunities.
- Take single-threaded ownership of core AI product areas, with end-to-end responsibility for customer success and business outcomes.
- Double as a Forward Deployed Engineer: Work regularly onsite with customers, and actually work with the systems.
- Every customer engagement becomes R&D for the platform, turning individual pain points into generalizable breakthroughs that serve the entire customer base

About us:
Data Science UA is a service company with strong data science and AI expertise. Our journey began in 2016 with uniting top AI talents and organizing the first Data Science tech conference in Kyiv. Over the past 9 years, we have diligently fostered one of the largest Data Science & AI communities in Europe.

About the client:
Our client's mission is to reduce the time and money enterprises spend on state-of-the-art technology by 99%. It is the core constraint that drives the product philosophy. It forces the company to reject incremental improvements and pursue fundamental breakthroughs. These principles are the operating system for how the company thinks, builds, and ships. They are how the company turns radical ambition into reality.

About the role:
We are looking for a Full Stack AI Engineer who will join the team. You'll be an architect of the revolution against enterprise tech waste. You'll build the core platform and intelligent agents that power the company's mission — not optimizing existing workflows, but eliminating them entirely.

Requirements:
- 5+ years of industry experience in product-centric full-stack engineering roles;
- Very strong software engineering background with deep expertise in Python/Typescript.
- Hands-on LLM product patterns (prompting, tool use, routing, evals, regression testing);
Experience of working with Agents & LLMs:
- LangGraph/LangChain (know its limitations & trade-offs);
- Multi-provider routing, prompt tooling, trace/eval discipline (LangSmith);

Experience of working with Knowledge & Search Technologies:
- Vector — pgvector on PostgreSQL (semantic);
- Graph traversal — Cypher on Neo4j (relationships & enterprise context graph);
- Keyword/BM25 — lexical precision;
- Cross-Encoder (BGE Reranker) + RRF (blend lists robustly);
Deep knowledge of working with Data & Databases:
- PostgreSQL (Cloud SQL) — primary OLTP;
- SQLAlchemy and BigQuery — analytics/outcomes & cost/waste telemetry;
- Redis (Memorystore) — caching + lightweight queue/broker;
- GCS — object storage (files, audio, artifacts);

Responsibilities:
Design & Build Autonomous Agents:
- Tackle the end-to-end challenge of creating agents that solve open-ended enterprise problems.
- Wrangling high-volume enterprise data, building robust evaluation frameworks.
- Mastering the interplay between LLM prompting, fine-tuning, and scalable system architecture.
- Lead Customer Conversations & Translate to Product: Work directly with customer executives to translate their most critical objectives into breakthrough product features.
- Build the "New": Own the most audacious hypotheses, like fully autonomous optimization agents that replace entire consulting engagements and obsolete the need for army-style deployments.
- Master Deep Context: Build systems that understand enterprise architecture patterns better than most architects, automatically discovering waste and optimization opportunities.
- Take single-threaded ownership of core AI product areas, with end-to-end responsibility for customer success and business outcomes.
- Double as a Forward Deployed Engineer: Work regularly onsite with customers, and actually work with the systems.
- Every customer engagement becomes R&D for the platform, turning individual pain points into generalizable breakthroughs that serve the entire customer base

About us:
Data Science UA is a service company with strong data science and AI expertise. Our journey began in 2016 with uniting top AI talents and organizing the first Data Science tech conference in Kyiv. Over the past 9 years, we have diligently fostered one of the largest Data Science & AI communities in Europe.

About the client:
Our client's mission is to reduce the time and money enterprises spend on state-of-the-art technology by 99%. It is the core constraint that drives the product philosophy. It forces the company to reject incremental improvements and pursue fundamental breakthroughs. These principles are the operating system for how the company thinks, builds, and ships. They are how the company turns radical ambition into reality.

About the role:
We are looking for a Full Stack AI Engineer who will join the team. You'll be an architect of the revolution against enterprise tech waste. You'll build the core platform and intelligent agents that power the company's mission — not optimizing existing workflows, but eliminating them entirely.

Requirements:
- 5+ years of industry experience in product-centric full-stack engineering roles;
- Very strong software engineering background with deep expertise in Python/Typescript.
- Hands-on LLM product patterns (prompting, tool use, routing, evals, regression testing);
Experience of working with Agents & LLMs:
- LangGraph/LangChain (know its limitations & trade-offs);
- Multi-provider routing, prompt tooling, trace/eval discipline (LangSmith);

Experience of working with Knowledge & Search Technologies:
- Vector — pgvector on PostgreSQL (semantic);
- Graph traversal — Cypher on Neo4j (relationships & enterprise context graph);
- Keyword/BM25 — lexical precision;
- Cross-Encoder (BGE Reranker) + RRF (blend lists robustly);
Deep knowledge of working with Data & Databases:
- PostgreSQL (Cloud SQL) — primary OLTP;
- SQLAlchemy and BigQuery — analytics/outcomes & cost/waste telemetry;
- Redis (Memorystore) — caching + lightweight queue/broker;
- GCS — object storage (files, audio, artifacts);

Responsibilities:
Design & Build Autonomous Agents:
- Tackle the end-to-end challenge of creating agents that solve open-ended enterprise problems.
- Wrangling high-volume enterprise data, building robust evaluation frameworks.
- Mastering the interplay between LLM prompting, fine-tuning, and scalable system architecture.
- Lead Customer Conversations & Translate to Product: Work directly with customer executives to translate their most critical objectives into breakthrough product features.
- Build the "New": Own the most audacious hypotheses, like fully autonomous optimization agents that replace entire consulting engagements and obsolete the need for army-style deployments.
- Master Deep Context: Build systems that understand enterprise architecture patterns better than most architects, automatically discovering waste and optimization opportunities.
- Take single-threaded ownership of core AI product areas, with end-to-end responsibility for customer success and business outcomes.
- Double as a Forward Deployed Engineer: Work regularly onsite with customers, and actually work with the systems.
- Every customer engagement becomes R&D for the platform, turning individual pain points into generalizable breakthroughs that serve the entire customer base

About us:
Data Science UA is a service company with strong data science and AI expertise. Our journey began in 2016 with uniting top AI talents and organizing the first Data Science tech conference in Kyiv. Over the past 9 years, we have diligently fostered one of the largest Data Science & AI communities in Europe.

About the client:
Our client's mission is to reduce the time and money enterprises spend on state-of-the-art technology by 99%. It is the core constraint that drives the product philosophy. It forces the company to reject incremental improvements and pursue fundamental breakthroughs. These principles are the operating system for how the company thinks, builds, and ships. They are how the company turns radical ambition into reality.

About the role:
We are looking for a Full Stack AI Engineer who will join the team. You'll be an architect of the revolution against enterprise tech waste. You'll build the core platform and intelligent agents that power the company's mission — not optimizing existing workflows, but eliminating them entirely.

Requirements:
- 5+ years of industry experience in product-centric full-stack engineering roles;
- Very strong software engineering background with deep expertise in Python/Typescript.
- Hands-on LLM product patterns (prompting, tool use, routing, evals, regression testing);
Experience of working with Agents & LLMs:
- LangGraph/LangChain (know its limitations & trade-offs);
- Multi-provider routing, prompt tooling, trace/eval discipline (LangSmith);

Experience of working with Knowledge & Search Technologies:
- Vector — pgvector on PostgreSQL (semantic);
- Graph traversal — Cypher on Neo4j (relationships & enterprise context graph);
- Keyword/BM25 — lexical precision;
- Cross-Encoder (BGE Reranker) + RRF (blend lists robustly);
Deep knowledge of working with Data & Databases:
- PostgreSQL (Cloud SQL) — primary OLTP;
- SQLAlchemy and BigQuery — analytics/outcomes & cost/waste telemetry;
- Redis (Memorystore) — caching + lightweight queue/broker;
- GCS — object storage (files, audio, artifacts);

Responsibilities:
Design & Build Autonomous Agents:
- Tackle the end-to-end challenge of creating agents that solve open-ended enterprise problems.
- Wrangling high-volume enterprise data, building robust evaluation frameworks.
- Mastering the interplay between LLM prompting, fine-tuning, and scalable system architecture.
- Lead Customer Conversations & Translate to Product: Work directly with customer executives to translate their most critical objectives into breakthrough product features.
- Build the "New": Own the most audacious hypotheses, like fully autonomous optimization agents that replace entire consulting engagements and obsolete the need for army-style deployments.
- Master Deep Context: Build systems that understand enterprise architecture patterns better than most architects, automatically discovering waste and optimization opportunities.
- Take single-threaded ownership of core AI product areas, with end-to-end responsibility for customer success and business outcomes.
- Double as a Forward Deployed Engineer: Work regularly onsite with customers, and actually work with the systems.
- Every customer engagement becomes R&D for the platform, turning individual pain points into generalizable breakthroughs that serve the entire customer base

About us:
Data Science UA is a service company with strong data science and AI expertise. Our journey began in 2016 with the uniting top AI talents and organizing the first Data Science tech conference in Kyiv. Over the past 9 years, we have diligently fostered one of the largest Data Science & AI communities in Europe.

About the client:
Our client is a quantitative digital assets market-neutral fund specializing in algorithmic trading across centralized and decentralized markets. The company designs, builds, and runs high-performance systems that power 24/7 trading strategies at scale. The edge is in combining advanced quantitative research with world-class engineering to capture opportunities in the rapidly evolving digital asset markets.

About the role:
We are looking for a Middle Python Developer with 3–4 years of experience in building and optimizing high-load, low-latency systems. This is a hands-on engineering role where you will enhance the execution platform and market data infrastructure, design and implement connectors for new centralized and decentralized exchanges, perform latency profiling and code optimization to achieve maximum throughput and minimal jitter, work with large-scale data pipelines to support real-time and historical analysis.

Requirements:
- 3–4 years of professional Python development in high-performance, high-load environments.
- Expert in concurrency and parallel programming (asyncio, multiprocessing, threading, Dask).
- Proficiency with scientific computing libraries: NumPy, Pandas, SciPy.
- Advanced skills in SQL and NoSQL databases;
- Deep expertise with ClickHouse or equivalent high-volume solutions.
- Strong knowledge of AWS (compute, storage, streaming, orchestration).
- Strong knowledge of messaging brokers (Kafka, RabbitMQ, ZeroMQ).
- Solid foundation in algorithms, data structures, and system design.
- Comfortable with Linux environments, Docker, Git.

Nice to have:
- Knowledge of Go for latency-critical modules.
- Experience with C++/Rust for ultra-low-latency systems.
- Familiarity with time-series analysis, quantitative finance, or crypto trading.
- Exposure to profiling and debugging tools (perf, flamegraphs, cProfile, PySpy).

Responsibilities:
- Develop and optimize Python-based trading infrastructure for high-performance execution.
- Implement and tune parallel and concurrent systems, using frameworks such as multiprocessing, threading, asyncio, aiohttp, Dask, joblib.
- Build and maintain data pipelines using NumPy, Pandas, SciPy, with storage in SQL and NoSQL databases (especially ClickHouse, PostgreSQL, Redis).
- Architect and scale systems on AWS, leveraging EC2, S3, Lambda, and related services.
- Design and integrate with messaging brokers (Kafka, RabbitMQ, ZeroMQ) for distributed trading workflows.
- Perform latency analysis, profiling, and performance optimization across trading components.
- Contribute to CI/CD, testing, and best practices in code quality.

The company offers:
- Directly shape the core trading infrastructure of a high-performing crypto hedge fund.
- Flat, high-impact environment where engineering directly drives P&L.
- Access to cutting-edge problems in latency optimization and distributed systems.

About us:
Data Science UA is a service company with strong data science and AI expertise. Our journey began in 2016 with the uniting top AI talents and organizing the first Data Science tech conference in Kyiv. Over the past 9 years, we have diligently fostered one of the largest Data Science & AI communities in Europe.

About the client:
Our client is a quantitative digital assets market-neutral fund specializing in algorithmic trading across centralized and decentralized markets. The company designs, builds, and runs high-performance systems that power 24/7 trading strategies at scale. The edge is in combining advanced quantitative research with world-class engineering to capture opportunities in the rapidly evolving digital asset markets.

About the role:
We are looking for a Middle Python Developer with 3–4 years of experience in building and optimizing high-load, low-latency systems. This is a hands-on engineering role where you will enhance the execution platform and market data infrastructure, design and implement connectors for new centralized and decentralized exchanges, perform latency profiling and code optimization to achieve maximum throughput and minimal jitter, work with large-scale data pipelines to support real-time and historical analysis.

Requirements:
- 3–4 years of professional Python development in high-performance, high-load environments.
- Expert in concurrency and parallel programming (asyncio, multiprocessing, threading, Dask).
- Proficiency with scientific computing libraries: NumPy, Pandas, SciPy.
- Advanced skills in SQL and NoSQL databases;
- Deep expertise with ClickHouse or equivalent high-volume solutions.
- Strong knowledge of AWS (compute, storage, streaming, orchestration).
- Strong knowledge of messaging brokers (Kafka, RabbitMQ, ZeroMQ).
- Solid foundation in algorithms, data structures, and system design.
- Comfortable with Linux environments, Docker, Git.

Nice to have:
- Knowledge of Go for latency-critical modules.
- Experience with C++/Rust for ultra-low-latency systems.
- Familiarity with time-series analysis, quantitative finance, or crypto trading.
- Exposure to profiling and debugging tools (perf, flamegraphs, cProfile, PySpy).

Responsibilities:
- Develop and optimize Python-based trading infrastructure for high-performance execution.
- Implement and tune parallel and concurrent systems, using frameworks such as multiprocessing, threading, asyncio, aiohttp, Dask, joblib.
- Build and maintain data pipelines using NumPy, Pandas, SciPy, with storage in SQL and NoSQL databases (especially ClickHouse, PostgreSQL, Redis).
- Architect and scale systems on AWS, leveraging EC2, S3, Lambda, and related services.
- Design and integrate with messaging brokers (Kafka, RabbitMQ, ZeroMQ) for distributed trading workflows.
- Perform latency analysis, profiling, and performance optimization across trading components.
- Contribute to CI/CD, testing, and best practices in code quality.

The company offers:
- Directly shape the core trading infrastructure of a high-performing crypto hedge fund.
- Flat, high-impact environment where engineering directly drives P&L.
- Access to cutting-edge problems in latency optimization and distributed systems.

About us:
Data Science UA is a service company with strong data science and AI expertise. Our journey began in 2016 with uniting top AI talents and organizing the first Data Science tech conference in Kyiv. Over the past 9 years, we have diligently fostered one of the largest Data Science & AI communities in Europe.

About the client:
Our client is the fast-moving remote team building AI-driven financial tools for the U.S. auto industry.
The product is oriented on the B2B segment, specifically on Sales and Marketing Managers in the US Automotive niche. The goal of the product is to eliminate existing data lack, providing a modern UI/UX experience.

About the role:
We are looking for a Full-Stack Engineer, who will join the team.

Requirements:
- 6+ years of experience in software engineering with a strong focus and 4+ years on Node/Next/Nest/Mongo/Service Architecture backend development.
- Solid knowledge of JavaScript/TypeScript and frameworks (React is a must, near pixel-perfect layout), Elastic UI Framework (replaced with Microsoft Fluent UI);
- Understanding of CI/CD pipelines and cloud services (AWS preferred).
- Knowledge of Tools & Processes: Jira/Confluence/Slack/Cursos;
- Clear and confident English communication skills (B2+ level).
- Proactive mindset, problem-solving attitude, and ability to work autonomously.
- Playwright usage for end-to-end tests.

Responsibilities:
- Build and maintain user-facing features with high performance and responsiveness.
- Collaborate with the product manager, QA engineers, and designers to deliver intuitive, impactful functionality.
- Contribute to architectural decisions and ensure clean, maintainable code across the stack.
- Optimize the app for maximum speed, performance, and scalability.
- Participate in code reviews, live coding sessions, and technical planning.
- Work independently and drive solutions end-to-end with minimal supervision.

About us:
Data Science UA is a service company with strong data science and AI expertise. Our journey began in 2016 with uniting top AI talents and organizing the first Data Science tech conference in Kyiv. Over the past 9 years, we have diligently fostered one of the largest Data Science & AI communities in Europe.

About the client:
Our client's mission is to reduce the time and money enterprises spend on state-of-the-art technology by 99%. It is the core constraint that drives the product philosophy. It forces the company to reject incremental improvements and pursue fundamental breakthroughs. These principles are the operating system for how the company thinks, builds, and ships. They are how the company turns radical ambition into reality.

About the role:
We are looking for a Full Stack AI Engineer who will join the team. You'll be an architect of the revolution against enterprise tech waste. You'll build the core platform and intelligent agents that power the company's mission — not optimizing existing workflows, but eliminating them entirely.

Requirements:
- 5+ years of industry experience in product-centric full-stack engineering roles;
- Very strong software engineering background with deep expertise in Python/Typescript.
- Hands-on LLM product patterns (prompting, tool use, routing, evals, regression testing);
Experience of working with Agents & LLMs:
- LangGraph/LangChain (know its limitations & trade-offs);
- Multi-provider routing, prompt tooling, trace/eval discipline (LangSmith);

Experience of working with Knowledge & Search Technologies:
- Vector — pgvector on PostgreSQL (semantic);
- Graph traversal — Cypher on Neo4j (relationships & enterprise context graph);
- Keyword/BM25 — lexical precision;
- Cross-Encoder (BGE Reranker) + RRF (blend lists robustly);
Deep knowledge of working with Data & Databases:
- PostgreSQL (Cloud SQL) — primary OLTP;
- SQLAlchemy and BigQuery — analytics/outcomes & cost/waste telemetry;
- Redis (Memorystore) — caching + lightweight queue/broker;
- GCS — object storage (files, audio, artifacts);

Responsibilities:
Design & Build Autonomous Agents:
- Tackle the end-to-end challenge of creating agents that solve open-ended enterprise problems.
- Wrangling high-volume enterprise data, building robust evaluation frameworks.
- Mastering the interplay between LLM prompting, fine-tuning, and scalable system architecture.
- Lead Customer Conversations & Translate to Product: Work directly with customer executives to translate their most critical objectives into breakthrough product features.
- Build the "New": Own the most audacious hypotheses, like fully autonomous optimization agents that replace entire consulting engagements and obsolete the need for army-style deployments.
- Master Deep Context: Build systems that understand enterprise architecture patterns better than most architects, automatically discovering waste and optimization opportunities.
- Take single-threaded ownership of core AI product areas, with end-to-end responsibility for customer success and business outcomes.
- Double as a Forward Deployed Engineer: Work regularly onsite with customers, and actually work with the systems.
- Every customer engagement becomes R&D for the platform, turning individual pain points into generalizable breakthroughs that serve the entire customer base

About us:
Data Science UA is a service company with strong data science and AI expertise. Our journey began in 2016 with uniting top AI talents and organizing the first Data Science tech conference in Kyiv. Over the past 9 years, we have diligently fostered one of the largest Data Science & AI communities in Europe.

About the client:
Our client's mission is to reduce the time and money enterprises spend on state-of-the-art technology by 99%. It is the core constraint that drives the product philosophy. It forces the company to reject incremental improvements and pursue fundamental breakthroughs. These principles are the operating system for how the company thinks, builds, and ships. They are how the company turns radical ambition into reality.

About the role:
We are looking for a Full Stack AI Engineer who will join the team. You'll be an architect of the revolution against enterprise tech waste. You'll build the core platform and intelligent agents that power the company's mission — not optimizing existing workflows, but eliminating them entirely.

Requirements:
- 5+ years of industry experience in product-centric full-stack engineering roles;
- Very strong software engineering background with deep expertise in Python/Typescript.
- Hands-on LLM product patterns (prompting, tool use, routing, evals, regression testing);
Experience of working with Agents & LLMs:
- LangGraph/LangChain (know its limitations & trade-offs);
- Multi-provider routing, prompt tooling, trace/eval discipline (LangSmith);

Experience of working with Knowledge & Search Technologies:
- Vector — pgvector on PostgreSQL (semantic);
- Graph traversal — Cypher on Neo4j (relationships & enterprise context graph);
- Keyword/BM25 — lexical precision;
- Cross-Encoder (BGE Reranker) + RRF (blend lists robustly);
Deep knowledge of working with Data & Databases:
- PostgreSQL (Cloud SQL) — primary OLTP;
- SQLAlchemy and BigQuery — analytics/outcomes & cost/waste telemetry;
- Redis (Memorystore) — caching + lightweight queue/broker;
- GCS — object storage (files, audio, artifacts);

Responsibilities:
Design & Build Autonomous Agents:
- Tackle the end-to-end challenge of creating agents that solve open-ended enterprise problems.
- Wrangling high-volume enterprise data, building robust evaluation frameworks.
- Mastering the interplay between LLM prompting, fine-tuning, and scalable system architecture.
- Lead Customer Conversations & Translate to Product: Work directly with customer executives to translate their most critical objectives into breakthrough product features.
- Build the "New": Own the most audacious hypotheses, like fully autonomous optimization agents that replace entire consulting engagements and obsolete the need for army-style deployments.
- Master Deep Context: Build systems that understand enterprise architecture patterns better than most architects, automatically discovering waste and optimization opportunities.
- Take single-threaded ownership of core AI product areas, with end-to-end responsibility for customer success and business outcomes.
- Double as a Forward Deployed Engineer: Work regularly onsite with customers, and actually work with the systems.
- Every customer engagement becomes R&D for the platform, turning individual pain points into generalizable breakthroughs that serve the entire customer base