AI ML Engineer

Job Purpose: Focuses on creating advanced machine learning models and AI-driven applications to solve complex business challenges. This position ensures the development of robust, scalable, and efficient systems for real-world deployment. The engineer will collaborate across teams to integrate AI solutions into production environments seamlessly. 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.

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