Machine Learning Engineer

Designs, develops, and deploys ML and GenAI models for customer solutions and platform components.

Responsibilities:

• Implement ML models from prototype to production.
• Fine-tune foundation models for domain-specific applications.
• Optimize performance and ensure scalability.
• Collaborate with MLOps and Data Engineers for deployment.

Mandatory Skills:

• ML algorithms (supervised, unsupervised, reinforcement learning).
• Feature engineering, vector embeddings, time-series, unstructured data (text, images, audio) analysis.
• Model optimization (hyperparameter tuning, regularization).
• Deep Learning (CNNs, RNNs, Transformers, Attention)
• Model optimization for inference (ONNX, TensorRT, quantization)
• A/B testing, ML interpretability (SHAP, LIME), ethical AI principles.

• Tools & Frameworks:

  Python, PyTorch, TensorFlow, HuggingFace, Scikit-learn, LangChain, LangGraph

• Soft Skills:

  Analytical thinking, problem solving, collaborative mindset, Good verbal and written Communication

Good to have:

• Generative AI:

  diffusion models, transformers, embeddings, fine-tuning (LoRA, PEFT).

• Agentic AI:

  task decomposition, planning, multi-agent orchestration
• Telco or Fintech domain