AI/ML Engineer

Primary Title (standardized):

Role & Responsibilities

• Design, prototype, and validate ML models (time-series, sequence, CV/NLP as needed) for aviation use-cases such as predictive maintenance, anomaly detection, and sensor fusion.
• Build reliable data ingestion and feature-engineering pipelines for both batch and streaming telemetry; ensure data quality and reproducibility.
• Productionize models: containerized serving, low-latency APIs, model versioning, and CI/CD for model rollouts and rollback strategies.
• Implement monitoring, performance tracing, and drift-detection; automate retraining pipelines and alerting to maintain model health in production.
• Collaborate with software, DevOps, and product teams to integrate ML into embedded and backend systems, balancing accuracy with latency and resource constraints.
• Drive engineering excellence through code reviews, unit/integration tests, documentation, and mentoring junior engineers.
  

Skills & Qualifications

• 2-4 years professional experience in ML/AI roles with strong Python coding skills and hands-on use of PyTorch or TensorFlow for model development.
• Proven experience deploying ML models to production using containerized workflows (Docker) and orchestration (Kubernetes) or equivalent on-prem/cloud setups.
• Solid ML fundamentals: supervised learning, evaluation metrics, feature engineering, and experience with time-series or sequence models.
• Experience with data engineering: SQL and Spark/PySpark (or similar), and working with high-frequency telemetry or IoT datasets.
  

Preferred

• Domain experience in aerospace, embedded systems, or real-time/edge inference; familiarity with sensor fusion and computer vision is a plus.
• Hands-on knowledge of MLOps and monitoring tools (CI/CD for models, model registries, Grafana/Prometheus, Evidently) and techniques for latency optimization (quantization/pruning).
  

Benefits & Culture Highlights

• Work on high-impact, safety-focused AI applications with opportunities to own end-to-end solutions from research to production.
• Collaborative, learning-oriented engineering culture with mentorship, technical autonomy, and career growth paths.
• Competitive compensation, modern on-site facilities in India, and regular technical knowledge-sharing forums.