Machine Learning Engineer (ML / DL / GenAI)

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 work on traditional predictive models (ML), Deep Learning architectures (DL), and Generative AI solutions integrated into enterprise applications. This role requires a “Full-Stack” ML mindset—spanning from data engineering and model training to API development, model deployment, 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 and Deep Learning architectures including CNNs, RNNs, and Transformers.

2. Generative AI (The Trend)

• Hands-on experience with LLM APIs and open-weight/open-source models.
• Advanced Prompt Engineering techniques including Chain-of-Thought, ReAct, and Few-shot prompting.
• Knowledge of Vector Stores and hybrid search techniques (Keyword + Semantic Search).

3. Engineering & Ops (The Scale)

• Experience with MLOps concepts, model lifecycle management, and production monitoring.
• Proficiency in SQL and distributed data processing frameworks.
• Familiarity with cloud platforms such as AWS, Azure, or GCP.
• Experience building and consuming REST APIs for ML services.