Growth through diversity, equity, and inclusion. As an ethical business, we do what is right — including ensuring equal opportunities and fostering a safe, respectful workplace for each of us. We believe diversity fuels both personal and business growth. We're committed to building an inclusive community where all our people thrive regardless of their backgrounds, identities, or other personal characteristics.
Freelance Cooperation – What Does It Mean?
- Open-ended freelance agreement. An active sole proprietorship (B2B) is required throughout the duration of the agreement.
- Part-time engagement. Work up to 50% of full-time workload, depending on current project needs.
- Flexibility that fits your schedule. We’ll reach out whenever a suitable project opportunity comes up. If you’re available and interested, you can choose to join the project. If the timing isn’t right, you’re always free to decline without terminating the agreement.
- No need to worry about the equipment. Freelancers typically work using equipment provided by us. Depending on the project, you may receive access to a virtual machine instead.
Requirements:
- 6+ years of experience in Data Science/AI engineering.
- At least 4+ years of experience in production-ready Python AI-related code development.
- At least 2+ years of experience in production-ready LLM-related code development, preferably based on the Retrieval-Augmented Generation (RAG) concept.
- Strong analytical and problem-solving skills with the ability to optimize AI solutions for diverse applications.
- Strong knowledge and experience in Generative AI, including LLMs, chatbots, AI agents, and RAG mechanisms.
- Deep understanding of LLM evaluators, validators, and guardrails.
- Hands-on experience with one or more GenAI frameworks: LangChain, LlamaIndex, LangGraph, or similar orchestration stacks.
- Hands-on experience designing or operating MCP servers/clients for LLM agents
- Strong Python skills, including production-grade code, packaging, and testing for data/ML services
- Solid understanding of ML/AI concepts: types of algorithms, machine learning frameworks, model efficiency metrics, model lifecycle, AI architectures.
- Proven ability to collaborate effectively across technical and non-technical teams.
- Familiarity with cloud environments such as Azure (preferred), GCP, or AWS, including AI-related managed services.
- Familiarity with CI/CD, testing, and containerized deployments.
- Excellent communication skills in English, with the ability to convey complex technical concepts to various audiences.
What we offer:
- Established global technology partner. Lingaro has been operating since 2008 and brings together 1,600+ professionals across 7 global locations.
- Flexible collaboration model and working hours. Enjoy working from inspiring locations in line with our remote work policy.
- A diverse, inclusive, and values-driven community.
- Autonomy to choose the way you work . We trust your ideas.
,[Lead discovery and solution design for GenAI use cases, translating business problems into concrete architectures (LLM decision, RAGs, fine-tuning, agents, guardrails)., Build end-to-end GenAI applications: data ingestion, retrieval layer, orchestration (e.g. LangChain/LlamaIndex/LangGraph), API/backend, and simple UI where needed., Design and implement RAG pipelines with vector databases, hybrid search, rerankers, query transformation, and evaluation frameworks for relevance and robustness., Perform model selection, prompting strategies, and fine-tuning (LoRA/QLoRA/SFT) for text, code, and multimodal models, including evaluation and A/B testing., Implement safety, compliance, and governance controls (input/output filters, PII handling, audit logs, human-in-the-loop review where required)., Collaborate with data engineers, product owners, and full-stack developers on scalable architectures, SLAs, and integration with existing enterprise systems., Gather technical requirements and estimate planned work., Mentor other data scientists/engineers in GenAI patterns, code quality, and best practices; contribute to internal libraries, templates, and reusable components., Stay current with GenAI landscape (new open and hosted models, agentic frameworks, evaluation techniques) and perform targeted PoCs to validate them.] Requirements: Data science, Generative AI Tools: .