AI ML Engineer

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.