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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