As a Mid‑Level Machine Learning Engineer, you’ll contribute to the design, development, and deployment of ML-driven features. You’ll work on pre‑training smaller models, implementing RAG pipelines for specific use cases, and deploying models to production with guidance from senior engineers

Key Responsibilities:

• Assist in building data pipelines for model training
• Run and monitor experiments for fine‑tuning pre‑trained transformer models
• Integrate vector stores into RAG workflows under guidance
• Fine‑tune or adapt retriever models (e.g., DPR) and generator models for domain tasks
• Develop REST or gRPC inference endpoints (FastAPI, Flask)
  Contribute to CI/CD pipelines for model builds and deployments (Gitlab CI/CD, Jenkins)
• Instrument inference services with basic monitoring (Prometheus metrics, Grafana dashboards)
• Support data labelling workflows and periodic retraining jobs
• Track experiments
• Work cross‑functionally with data scientists, DevOps, and product teams

• 1–2 years in applied machine learning or data science roles.
• Hands‑on with at least one deep learning framework
• Experience with NLP and fine‑tuning transformer models via Hugging Face
• Familiarity with FAISS or Pinecone integrations
• Python, Docker, basic Kubernetes concepts, FastAPI/Flask
• MLOps: Exposure to CI/CD for ML, experiment tracking

• Experience with distributed training
• Familiarity with secure model serving or data privacy techniques