Key responsibilities

Model & Solution Engineering  Translate business problems into ML formulations; select suitable architectures (e.g., gradient boosting, transformers) with clear success metrics.  Build end-to-end pipelines: feature extraction, training, hyperparameter tuning, and packaging models as reproducible artifacts.  Optimize inference (quantization, distillation, mixed precision) for latency and throughput on CPU/GPU.  Conduct evaluation beyond accuracy (calibration, fairness, cost-sensitive metrics, PR/ROC under imbalance).
MLOps, Deployment & Observability  Implement model versioning, lineage, and experiment tracking; manage rollbacks and canary releases.  Build real-time and batch inference services; integrate with message buses and vector databases.  Monitor for schema checks, data drift, performance regression, and cost observability.  Create alerting and autoscaling policies tied to SLAs, maintain incident runbooks for model services
Data Engineering, Quality & Governance  Design data contracts; implement ETL/ELT pipelines (e.g., Spark/Databricks) with testing and backfills.  Enforce data quality gates and schema evolution strategies to prevent mismatches.  Apply privacy-by-design: PII handling, tokenization, and secure secrets management.  Collaborate on cost-efficient data architectures (tiering, caching, Parquet/Delta formats)
Experimentation, Product Integration & Stakeholder Enablement  Design experiments (A/B, counterfactual evaluation); define guardrails and success criteria with product teams.  Integrate models via APIs/SDKs with business rules and fallbacks for graceful degradation.  Produce clear documentation (model cards, decision logs) and present trade-offs to stakeholders.

Qualifications & Skills Bachelor’s or Master’s degree in Computer Science, Data Science, AI/ML, or a related field.

 Proven experience in designing, training, and deploying machine learning models and AI solutions.

 Strong programming skills in Python and familiarity with ML frameworks (TensorFlow, PyTorch, Scikit-learn).

 Hands-on experience with MLOps tools and practices (Docker, Kubernetes, MLflow, CI/CD pipelines).

 Proficiency in data processing and ETL tools (Spark, Databricks) and working with large datasets.

 Knowledge of model optimization techniques (quantization, distillation) and performance tuning for production environments.

 Familiarity with cloud platforms (Azure, AWS, or GCP) and scalable architecture design.

 Understanding of data governance, privacy standards, and compliance requirements.

 Strong analytical and problem-solving skills with attention to detail.

 Excellent communication skills to collaborate with cross-functional teams and present technical concepts clearly.

Key responsibilities

Model & Solution Engineering  Translate business problems into ML formulations; select suitable architectures (e.g., gradient boosting, transformers) with clear success metrics.  Build end-to-end pipelines: feature extraction, training, hyperparameter tuning, and packaging models as reproducible artifacts.  Optimize inference (quantization, distillation, mixed precision) for latency and throughput on CPU/GPU.  Conduct evaluation beyond accuracy (calibration, fairness, cost-sensitive metrics, PR/ROC under imbalance).
MLOps, Deployment & Observability  Implement model versioning, lineage, and experiment tracking; manage rollbacks and canary releases.  Build real-time and batch inference services; integrate with message buses and vector databases.  Monitor for schema checks, data drift, performance regression, and cost observability.  Create alerting and autoscaling policies tied to SLAs, maintain incident runbooks for model services
Data Engineering, Quality & Governance  Design data contracts; implement ETL/ELT pipelines (e.g., Spark/Databricks) with testing and backfills.  Enforce data quality gates and schema evolution strategies to prevent mismatches.  Apply privacy-by-design: PII handling, tokenization, and secure secrets management.  Collaborate on cost-efficient data architectures (tiering, caching, Parquet/Delta formats)
Experimentation, Product Integration & Stakeholder Enablement  Design experiments (A/B, counterfactual evaluation); define guardrails and success criteria with product teams.  Integrate models via APIs/SDKs with business rules and fallbacks for graceful degradation.  Produce clear documentation (model cards, decision logs) and present trade-offs to stakeholders.