Softvery Solutions

Вимоги

Python 3.11/3.12, FastAPI, SQLAlchemy 2, PostgreSQL, Alembic
Microservices architecture, Docker/docker-compose, Kubernetes + Helm

Frontend:
TypeScript, modern tooling (Vite/Vitest, ESLint) — greenfield, so the candidate can help shape the framework choice
Nice to have: MLflow, Elasticsearch, SonarQube, strong testing culture (pytest, mypy, ruff, coverage gates

Буде плюсом

AI/GenAI side:
LangChain / LangGraph agent workflows, RAG with Weaviate (vector DB)
LLM integration via OpenAI-compatible APIs, text-to-SQL, Trino, OpenMetadata
 

Вимоги

Python 3.11/3.12, FastAPI, SQLAlchemy 2, PostgreSQL, Alembic
Microservices architecture, Docker/docker-compose, Kubernetes + Helm

Frontend:
TypeScript, modern tooling (Vite/Vitest, ESLint) — greenfield, so the candidate can help shape the framework choice
Nice to have: MLflow, Elasticsearch, SonarQube, strong testing culture (pytest, mypy, ruff, coverage gates

Буде плюсом

AI/GenAI side:
LangChain / LangGraph agent workflows, RAG with Weaviate (vector DB)
LLM integration via OpenAI-compatible APIs, text-to-SQL, Trino, OpenMetadata
 

Client
Global organization providing scientific, medical, and analytics information platforms.

The Role
We are looking for a Senior Software Engineer with strong backend expertise and hands-on experience building AI-powered platforms. This role combines software engineering, cloud-native architecture, and generative AI enablement.
You will collaborate with engineering, product, and research stakeholders to transform experimental AI concepts into reliable production services while helping drive engineering excellence across the organization.


What We’re Looking For
- 10+ years of professional software engineering experience.
- Strong programming expertise in Python and Java.
- Solid understanding of software architecture, system design, and data modeling.
- Experience building backend services, APIs, and distributed systems.
- Hands-on knowledge of modern AI frameworks and orchestration tools such as LangChain, LangGraph, or similar technologies.
- Experience working with Kubernetes, preferably within AWS EKS environments.
- Proven ability to design and deliver scalable, production-grade applications.
- Experience influencing technical direction and contributing to architectural decisions.
- Strong communication skills and ability to collaborate across multidisciplinary teams.


Responsibilities
- Build and maintain reusable AI services, APIs, and platform components primarily using Python, with occasional contributions in Java.
- Design backend systems that support scalability, reliability, and long-term maintainability.
- Develop cloud-native applications running in containerized environments.
- Contribute to the architecture of distributed systems and shared platform capabilities.
- Partner with AI and research teams to operationalize machine learning and generative AI solutions.
- Transform proof-of-concept models into production-ready services.
- Support the integration of LLM-powered functionality across multiple products.
- Establish and promote best practices related to software design, testing, observability, deployment automation, and operational reliability.
- Participate in architecture discussions, technical planning, and code reviews.
- Contribute to continuous improvement initiatives across engineering teams.
- Work closely with cross-functional stakeholders in an Agile environment.
- Provide technical guidance and mentorship to less experienced engineers.
- Share knowledge around backend architecture, platform engineering, and AI development patterns.
- Stay informed about advancements in generative AI, platform technologies, and cloud ecosystems.
- Evaluate emerging technologies and recommend practical adoption strategies where appropriate.


Nice to Have
- Experience operationalizing LLMs or building internal AI platforms.
- Familiarity with observability practices (metrics, logging, alerts).
- Exposure to knowledge graphs or semantic search systems.
- Experience building internal AI platforms or AI infrastructure products.
- Familiarity with deploying and managing large language model workloads.
- Knowledge of monitoring, logging, alerting, and operational observability practices.
- Exposure to semantic search, vector databases, or knowledge graph technologies.
- Experience supporting AI systems in production environments.
 

Client
Global organization providing scientific, medical, and analytics information platforms.

The Role
We are looking for a Senior Software Engineer with strong backend expertise and hands-on experience building AI-powered platforms. This role combines software engineering, cloud-native architecture, and generative AI enablement.
You will collaborate with engineering, product, and research stakeholders to transform experimental AI concepts into reliable production services while helping drive engineering excellence across the organization.


What We’re Looking For
- 10+ years of professional software engineering experience.
- Strong programming expertise in Python and Java.
- Solid understanding of software architecture, system design, and data modeling.
- Experience building backend services, APIs, and distributed systems.
- Hands-on knowledge of modern AI frameworks and orchestration tools such as LangChain, LangGraph, or similar technologies.
- Experience working with Kubernetes, preferably within AWS EKS environments.
- Proven ability to design and deliver scalable, production-grade applications.
- Experience influencing technical direction and contributing to architectural decisions.
- Strong communication skills and ability to collaborate across multidisciplinary teams.


Responsibilities
- Build and maintain reusable AI services, APIs, and platform components primarily using Python, with occasional contributions in Java.
- Design backend systems that support scalability, reliability, and long-term maintainability.
- Develop cloud-native applications running in containerized environments.
- Contribute to the architecture of distributed systems and shared platform capabilities.
- Partner with AI and research teams to operationalize machine learning and generative AI solutions.
- Transform proof-of-concept models into production-ready services.
- Support the integration of LLM-powered functionality across multiple products.
- Establish and promote best practices related to software design, testing, observability, deployment automation, and operational reliability.
- Participate in architecture discussions, technical planning, and code reviews.
- Contribute to continuous improvement initiatives across engineering teams.
- Work closely with cross-functional stakeholders in an Agile environment.
- Provide technical guidance and mentorship to less experienced engineers.
- Share knowledge around backend architecture, platform engineering, and AI development patterns.
- Stay informed about advancements in generative AI, platform technologies, and cloud ecosystems.
- Evaluate emerging technologies and recommend practical adoption strategies where appropriate.


Nice to Have
- Experience operationalizing LLMs or building internal AI platforms.
- Familiarity with observability practices (metrics, logging, alerts).
- Exposure to knowledge graphs or semantic search systems.
- Experience building internal AI platforms or AI infrastructure products.
- Familiarity with deploying and managing large language model workloads.
- Knowledge of monitoring, logging, alerting, and operational observability practices.
- Exposure to semantic search, vector databases, or knowledge graph technologies.
- Experience supporting AI systems in production environments.