In this FDE role, the engineer manages the full ML lifecycle from opportunity discovery and feasibility assessment through model deployment and ongoing monitoring with strong ownership at every stage.

The role demands both deep technical capability and the ability to communicate model decisions, trade-offs, and constraints clearly to non-technical stakeholders, building confidence in AI solutions through transparency and measurable outcomes.

Required Skills & Expertise :

- Python as primary ML language; PyTorch or TensorFlow; scikit-learn for classical ML and baselines

- Tree-based models (XGBoost, LightGBM, Random Forests) and deep learning architectures(CNNs, RNNs, Transformers)

- Exploratory data analysis and visualization : pandas, matplotlib, seaborn, or Plotly for insight derivation

- SQL and PySpark/Databricks for large-scale data processing; Parquet and similar analytical formats

- MLOps : MLflow or equivalent for experiment tracking and model lifecycle; Docker; REST/gRPCAP Is for model serving

- LLMs, RAG, fine-tuning, prompt engineering, and hybrid AI/ML architectures; understanding of when each approach applies

Core Responsibilities :

- Identify ML opportunities in customer processes; profile data quality and availability

- Prototype rapidly to validate technical feasibility before full model investment; communicate what is and is not achievable given data constraints

- Design, implement, and optimize machine learning algorithms, data pipelines, and AI services for scalable production deployment

- Run experiments, evaluate models, and deploy to cloud environments with robust observability, monitoring, and drift detection

- Collaborate with engineering, product, and architecture teams; explain results and trade-offs to both technical and business audiences

- Ensure responsible AI practices : data governance, PII compliance, auditability, and bias awareness throughout the model lifecycle