Job Description
Tiger Analytics is looking for a skilled and innovative
Machine Learning Engineer
with hands‑on experience in
Google Cloud Platform (GCP)
and
Vertex AI
to design, build, and deploy scalable ML solutions. You will play a key role in operationalizing machine learning models and driving the end‑to‑end ML lifecycle, from data ingestion to model serving and monitoring.
Key Responsibilities:
Develop, train, and optimize ML models using
Vertex AI , including Vertex Pipelines, AutoML, and custom model training.
Design and build scalable ML pipelines for feature engineering, training, evaluation, and deployment.
Deploy models to production using Vertex AI endpoints and integrate with downstream applications or APIs.
Collaborate with data scientists, data engineers, and MLOps teams to enable reproducible and reliable ML workflows.
Monitor model performance and set up alerting, retraining triggers, and drift detection mechanisms.
Utilize GCP services such as
BigQuery, Dataflow, Cloud Functions, Pub/Sub , and
GCS
in ML workflows.
Apply CI/CD principles to ML models using
Vertex AI Pipelines ,
Cloud Build , and
GitOps
practices.
Implement model governance, versioning, explainability, and security best practices within Vertex AI.
Document architecture decisions, workflows, and model lifecycle clearly for internal stakeholders.
1. Advanced Generative AI
Advanced RAG including Graph based hybrid retrieval
Multimodal agent
Deep knowledge on ADK , Langchain Agentic Frameworks
Fine tuning and Distillation
2. Python Expertise
Expert in Python with strong OOP and functional programming skills
Proficient in ML/DL libraries: TensorFlow, PyTorch, scikit‑learn, pandas, NumPy, PySpark
Experience with production‑grade code, testing, and performance optimization
3. GCP Cloud Architecture & Services
Proficiency in GCP services such as Vertex AI, BigQuery, Cloud Storage, Cloud Run, Cloud Functions, Pub/Sub, Dataproc, Dataflow, Understanding of IAM, VPC
6. API Development & Integration
Designs and builds RESTful APIs using FastAPI or Flask
Integrates ML models into APIs for real‑time inference
Implements authentication, logging, and performance optimization
7. System Design & Scalability
Designs end‑to‑end AI systems with scalability and fault tolerance in mind
Hands‑on experience in developing distributed systems, microservices, and asynchronous processing
Benefits
Significant career development opportunities exist as the company grows. The position offers a unique opportunity to be part of a small, fast‑growing, challenging and entrepreneurial environment, with a high degree of individual responsibility.
Tiger Analytics provides equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, pregnancy, national origin, ancestry, marital status, protected veteran status, disability status, or any other basis as protected by federal, state, or local law.
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