Senior ML Engineer

Role & responsibilities

Role : Senior ML Engineer

Company : Feuji Software Solutions Pvt Ltd.,

Mode of Hire : Permanent Position

Experience : 6 10 Years

Work Location : Hyderabad

About Feuji

Feuji, established in 2014 and headquartered in Dallas, Texas, has rapidly emerged as a leading global technology services provider. With strategic locations including a Near Shore facility in San Jose, Costa Rica, and Offshore Delivery Centers in Hyderabad, and Bangalore, we are well-positioned to cater to a diverse clientele. Our team of 600 talented engineers drives our success, delivering innovative solutions to our clients and contributing to our recognition as a 'Best Place to Work For.'

We collaborate with a wide range of clients, from startups to industry giants in sectors like Healthcare, Education, IT, and engineering, enabling transformative changes in their operations. Through partnerships with top technology providers such as AWS, Checkpoint, Gurukul, CoreStack, Splunk, and Micro Focus, we empower our clients' growth and innovation. With a clientele including Microsoft, HP, GSK, and DXC Technologies, we specialize in managed cloud services, cybersecurity, Product and Quality Engineering Services, and Data and Insights solutions, tailored to drive tangible business outcomes.

Our commitment to creating 'Happy Teams' underscores our values and dedication to positive impact. Feuji welcomes exceptional talent to join our team, offering a platform for growth, development, and a culture of innovation and excellence.

About the Role:

Leads the design, development, and scaling of end-to-end ML systems with a focus on automation, governance, and performance optimization. Architects MLOps platforms for model versioning, drift detection, and deployment orchestration across cloud environments. Ensures data quality, cost efficiency, and regulatory compliance while mentoring teams and driving best practices in production ML engineering.

Key Responsibilities

ML Model Development

• Design, develop, and deploy production ML models (supervised, unsupervised, RL, deep learning).
• Feature engineering, model selection, hyperparameter tuning, and evaluation.
• Ensure model interpretability (SHAP, LIME) and explainability.
• Vectorization, embedding stores, sparse features.
• Online/offline consistency between feature stores.

MLOps & ML Infrastructure

• Build automated ML pipelines: training validation deployment retraining.
• Implement experiment tracking, model versioning, and registry (MLflow, W&B, Neptune).
• Deploy feature stores and model serving infrastructure (TensorFlow Serving, TorchServe, Triton, BentoML).
• Orchestrate workflows using Kubeflow, Airflow, or Metaflow.
• Implement A/B testing, canary deployments, and drift detection.
• Ensure model governance, lineage tracking, and compliance.

Model Monitoring & Alerting

• Build monitoring pipelines for model performance: latency, accuracy, data/concept drift.
• Deploy dashboards (Grafana, Kibana, CloudWatch) and alerts (Slack, PagerDuty).
• Automate prediction and actuals logging for continuous monitoring

Data Engineering

• Advanced SQL: joins, aggregations, CTEs, window functions.
• Work with cloud data platforms (Databricks, Snowflake, BigQuery, Redshift, Synapse).
• Build ETL/ELT pipelines using AWS Glue, dbt, or Airflow.
• Data validation frameworks: Great Expectations, Deequ, TFDV
• Data lineage/governance: DataHub, MLflow lineage, Databricks Unity Catalog, Atlas
• Bias & fairness tools: AIF360, Fairlearn

Platform Engineering & Observability

• Build self-service ML platforms on Kubernetes/EKS/OpenShift.
• Track cloud cost efficiency and resource consumption (CUR, Kubecost).

Required Skills:

• Strong hands-on experience with an ability to lead a team

ML & Data Science

• EDA, ML algorithms: regression, classification, clustering, ensembles (XGBoost, LightGBM, CatBoost).
• Deep learning: any of TensorFlow, PyTorch, Keras (CNNs, RNNs, Transformers).
• Model evaluation, cross-validation, and interpretability.

MLOps

• Experiment tracking & model registry (any of MLflow, W&B, Neptune).
• Feature stores (any of Feast, Tecton, SageMaker Feature Store).
• Model serving & inference optimization (any of TorchServe, Triton, BentoML, KServe).
• ML orchestration (any of Kubeflow, Airflow, Prefect, Metaflow).
• Containerization (Docker) and Kubernetes for ML workloads.

Programming & Automation

• Strong Python, Bash scripting
• Incident response and troubleshooting

Nice to Have Skills :

• Cloud platforms: AWS/GCP/Azure (including ML services: SageMaker, Vertex AI, Azure ML).
• Distributed training (Horovod, PyTorch DDP, Ray)
• Model optimization (quantization, pruning, distillation)
• AutoML and neural architecture search
• Open-source ML contributions