Hire AI-Enabled Data Scientists
Who Deliver Results

India produces some of the strongest production ML and data engineering talent globally, specialists in Python, TensorFlow, PyTorch, LangChain, and MLOps. Devlyn combines this talent with AI-driven development workflows, so you get senior data scientists who ship models faster than traditional offshore teams. English-first communication, US and EU timezone overlap, and transparent rate cards mean no coordination overhead and no billing surprises.

Three things no competitor commits to: senior-only engineers (5–10+ years, no junior data scientists on your production ML), weekly demos (working deployed models or pipelines every Friday, not a status deck), and AI-driven delivery with human review on every output before it reaches production. Most offshore data science agencies claim quality. Devlyn builds accountability into the engagement model.

From first conversation to onboarded engineer in 48 hours. We match you with pre-vetted profiles, you interview directly, and your data scientist joins your sprint within two business days. No 3–6 month hiring cycle. No compromise on seniority or specialization.

Our data scientists use AI tools for EDA automation, feature engineering scaffolding, model iteration, and documentation. Every AI output is reviewed by a senior engineer before it touches your pipeline or production endpoint. The result: model iterations that took weeks take days. Architecture decisions and deployment ownership stay with the human engineer. AI accelerates throughput; it does not replace judgment on model design.

Full coverage: Scikit-learn, XGBoost, TensorFlow, PyTorch for classical ML and deep learning. LangChain, LlamaIndex, HuggingFace, FAISS, Pinecone for LLM and RAG engineering. MLflow, SageMaker, Vertex AI, Kubeflow for MLOps. Apache Airflow, Spark, dbt, Snowflake for data engineering. FastAPI inference backends for production model serving.

It depends on your product stage and what you are trying to build. Data scientists answer business questions and build prediction models. ML engineers own production model serving and monitoring. Data engineers build the pipelines that feed everything. For most seed-to-Series B companies, a production-oriented senior data scientist covers all three at pragmatic depth. We scope this with you on the strategy call before you commit to anything.

Every Devlyn data science engagement runs under a structured delivery model: weekly demos showing working, deployed models and pipelines; code reviews on every PR; models tested in staging before production; monitoring configured from day one. AI-generated outputs are reviewed by senior engineers before merge. You see what shipped every Friday, not a weekly status email.

We replace the engineer at no cost. If the fit is not right in the first week, tell us, we rematch and onboard a replacement without delay, with full context handover so your ML work does not stop. No lock-in. No severance risk. Your models keep moving.