Key responsibilities
1) 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).
2) 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.
3) 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).
4) 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.