Machine Learning Engineer (Mid–Senior level)

About the roleWe’re building a suite of products that powers multiple customer-facing and internal capabilities across enterprise workflows.

We’re looking for experienced ML engineers (mid-level senior and above) who can design, build, and ship ML systems end-to-end—not just run notebooks or stitch together managed services.This role is for engineers who enjoy owning systems from first principles: data pipelines, training/evaluation, deployment/serving, monitoring, and continuous improvement—at real scale, with real reliability and security constraints.What you’ll doBuild and productionize ML solutions end-to-end: data → training → evaluation → deployment → monitoring → iteration.Design and implement scalable model-serving systems (batch + real-time) with clear SLAs (latency, throughput, availability, cost).Create and maintain ML infrastructure components from scratch (not only “using cloud as-is”):Containerized services, inference APIs, autoscaling patterns.Orchestration for training/inference workflows.Feature/data processing pipelines.CI/CD for ML, experiment tracking, model registry, reproducibility.Define strong evaluation methodologies and quality gates (offline metrics, online testing, regression prevention).Implement observability (logs/metrics/traces), drift monitoring, and safe rollback mechanisms.Collaborate with engineering/product to translate real business problems into ML solutions that are reliable, secure, and maintainable.

Seniority / experienceWe are hiring mid-level senior and above candidates.Demonstrated experience shipping production ML systems (not just prototypes).Core technical skillsStrong Python skills for production (clean code, tests, packaging, performance, code reviews).Solid understanding of ML fundamentals (supervised learning, evaluation, bias/variance tradeoffs, error analysis).Strong engineering system design skills for ML applications (data flow, compute, storage, latency/cost tradeoffs).Experience with modern ML frameworks and tooling (examples: PyTorch / TensorFlow, scikit-learn, etc.).Hands-on experience in at least one major cloud (AWS/Azure/GCP) building real systems.Proven ability to build core services/components, such as:Model deployment & serving (REST/gRPC), containers, Kubernetes and/or equivalent.Workflow orchestration (e.g., Airflow/Prefect/Argo or similar).Data pipelines (stream/batch), storage patterns, schema/versioning.Monitoring / alerting for ML systems (quality + infra metrics).Strong understanding of security basics for production services (secrets, access control, secure deployment patterns).Strong testing culture (unit/integration), contract testing for ML APIs, load testing.Designing reusable internal libraries/SDKs for other teams.Building feature stores, embedding pipelines, or retrieval-augmented systems.Experience with MLOps (model registry, experiment tracking, lineage, reproducibility).Performance tuning for inference (batching, quantization, caching, GPU utilization).Hybrid deployment constraints (on-prem, private cloud, air-gapped, data residency).Applied domain experience in majority of the following:· NLP / multilingual text systems (classification, extraction, retrieval, summarization, evaluation).· Computer vision / document understanding / OCR + post-processing pipelines.· Entity resolution, similarity matching, deduplication, record linkage.· Anomaly detection, risk scoring, fraud signals, graph-based features.· Search / retrieval systems (embeddings, vector databases, reranking).