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.