Machine Learning Engineer Mid Level

Job Title: Machine Learning Engineer (ML / DL / GenAI)
Level: Mid-Level (3–6 years of experience)
Department: AI & Data Science Engineering

Role Overview
As a Mid-level ML Engineer, you will be a key driver in designing, developing, and deploying production-grade AI systems. You will not only work on traditional predictive models (ML) and neural networks (DL) but also lead the integration of Generative AI and Large Language Models (LLMs) into our core products. This role requires a "Full-Stack" ML mindset—spanning from data engineering and model training to MLOps and GenAI orchestration.

Required Technical Skills
1. Machine Learning & Deep Learning (The Foundation)
• Strong proficiency in Python and its ecosystem (Pandas, NumPy, Scipy).
• Deep expertise in PyTorch or TensorFlow/Keras.
• Experience with classical ML algorithms (Trees, SVMS, Clustering) and Deep Learning (CNNs, RNNs, Transformers).

2. Generative AI (The Trend)
• Hands-on experience with LLM APIs (OpenAI, Anthropic, Gemini) and Open-Weight models.
• Advanced Prompt Engineering (Chain-of-Thought, ReAct, Few-shot prompting).
• Knowledge of Vector Store management and hybrid search (Keyword + Semantic).

3. Engineering & Ops (The Scale)
• Experience with MLOps platforms (Weights & Biases, MLflow, or SageMaker).
• Proficiency in SQL and distributed data processing (Spark/Dask).
• Familiarity with cloud providers (AWS, Azure, or GCP).

Preferred Qualifications
• Education: Master’s or PhD in Computer Science, AI, Mathematics, or a related field (or equivalent industry experience).
• Proven Impact: Has deployed at least 2–3 ML/GenAI models into a production environment with real-world users.
• Research to Prod: Ability to read a research paper and implement the core concepts in code.
• GenAI Specialized: Experience with Multi-modal models (Vision-Language) or specialized fine-tuning.