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.

Generative AI LLM Orchestration: Design and implement Retrieval-Augmented Generation (RAG) pipelines and Agentic workflows using modern orchestration frameworks.

Model Optimization Fine-tuning: Select, evaluate, and fine-tune Large Language Models (LLMs) using parameter-efficient tuning techniques.

End-to-End ML/DL Development: Build and maintain traditional ML models and Deep Learning architectures for classification, regression, computer vision, and NLP tasks.

API Development Real-Time Model Serving: Develop scalable APIs for AI/ML services using frameworks such as FastAPI or Flask and enable real-time model inference and streaming integrations using technologies like Apache Kafka.

MLOps Deployment: Deploy and monitor ML/GenAI models in production environments using containerization and CI/CD best practices.

Evaluation Observability: Implement evaluation frameworks to measure model quality, relevance, reliability, and hallucination rates for GenAI applications.

Data Infrastructure: Work with Vector Databases and semantic search systems to manage embeddings and optimize retrieval performance.

AI Safety Governance: Implement responsible AI practices, including PII protection, prompt safety, and bias mitigation strategies.