Data Scientist (AI/ML) — Mid-level (3–5 years)

ABOUT THE ROLE

We build agentic AI applications for large-scale enterprise clients, including network support systems, recommendation engines, etc.. You'll work at the intersection of applied machine learning and production software — fine-tuning LLMs, routing between agents, and building retrieval pipelines that solve real operational problems. This is a hands-on engineering role, not a research position. You will own solutions end-to-end, from prototype to production.

RESPONSIBILITIES- Design, fine-tune, and evaluate AI solutions for domain-specific tasks (network support, ops automation)- Build and maintain LLM routing logic — selecting and orchestrating between multiple model providers based on task, cost, and latency requirements- Integrate LLMs into agentic AI workflows and orchestration layers- Implement RAG pipelines including chunking strategies, embedding models, and vector store management when retrieval is required- Write clean, production-ready Python code following modern project tooling standards (Hatch, pyproject.toml)- Collaborate with engineering, product, and client stakeholders to translate business requirements into data and model solutions- Evaluate model performance using rigorous metrics; run A/B tests and regression tests on model changes- Monitor deployed models in production and respond to drift, degradation, or failure modes- Document technical decisions and contribute to internal knowledge sharing

REQUIRED SKILLS

Languages & tooling: Python, Hatch, SQL,GitLLM & AI: Fine-tuning, LLM routing, Prompt engineering, RAG, Embeddings, Agentic workflows

EXPERIENCE REQUIREMENTS-

3–5 years of data science or ML engineering experience in an industry setting-

Demonstrated experience deploying models or AI systems to production (not just notebooks or prototypes)-

Hands-on experience with at least two major LLM providers (OpenAI, Anthropic, Mistral, Llama,

etc.)- Experience working within a client-delivery or product team environment-

Familiarity with modern Python project structure and packaging (pyproject.toml, virtual environments)

NICE TO HAVE- Experience in telecom, networking, or infrastructure domains- Familiarity with agentic frameworks (AutoGen, CrewAI, custom tool-use patterns)- Experience working with or alongside large tech clients- Contributions to open-source ML tooling

WHAT WE'RE NOT LOOKING FOR

This is not a research role. We are not seeking candidates whose primary experience is publishing papers, developing novel algorithms, or academic benchmarking. We need practitioners who have shipped and maintained AI systems in production.