Job Description

Job Category: AI & Data Science
Degree Level: Bachelor's Degree
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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.

Skills Required