At a glanceSummarised by Seekless from the posting.
Skills
GCP
Vertex AI
BigQuery
Dataflow
Cloud Functions
Pub/Sub
GCS
TensorFlow
PyTorch
scikit-learn
pandas
NumPy
PySpark
FastAPI
Flask
Cloud Run
Dataproc
IAM
VPC
ADK
Langchain
Cloud Build
GitOps
About the role
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:
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Develop, train, and optimize ML models using Vertex AI, including Vertex Pipelines, AutoML, and custom model training.
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Design and build scalable ML pipelines for feature engineering, training, evaluation, and deployment.
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Deploy models to production using Vertex AI endpoints and integrate with downstream applications or APIs.
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Collaborate with data scientists, data engineers, and MLOps teams to enable reproducible and reliable ML workflows.
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Monitor model performance and set up alerting, retraining triggers, and drift detection mechanisms.
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Utilize GCP services such as BigQuery, Dataflow, Cloud Functions, Pub/Sub, and GCS in ML workflows.
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Apply CI/CD principles to ML models using Vertex AI Pipelines, Cloud Build, and GitOps practices.
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Implement model governance, versioning, explainability, and security best practices within Vertex AI.
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Document architecture decisions, workflows, and model lifecycle clearly for internal stakeholders.
Requirements
1.
Advanced Generative AI
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Advanced RAG including Graph based hybrid retrieval
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Multimodal agent
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Deep knowledge on ADK , Langchain Agentic Frameworks
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Fine tuning and Distillation
2.
Python Expertise
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Expert in Python with strong OOP and functional programming skills
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Proficient in ML/DL libraries: TensorFlow, PyTorch, scikit-learn, pandas, NumPy, PySpark
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Experience with production-grade code, testing, and performance optimization
3.
GCP Cloud Architecture & Services
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Proficiency in GCP services such as:
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Vertex AI
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BigQuery
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Cloud Storage
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Cloud Run
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Cloud Functions
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Pub/Sub
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Dataproc
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Dataflow
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Understanding of IAM, VPC
6.
API Development & Integration
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Designs and builds RESTful APIs using FastAPI or Flask
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Integrates ML models into APIs for real-time inference
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Implements authentication, logging, and performance optimization
7.
System Design & Scalability
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Designs end-to-end AI systems with scalability and fault tolerance in mind
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Hands-on experience in developing distributed systems, microservices, and asynchronous processing
Benefits
This position offers an excellent opportunity for significant career development in a fast-growing and challenging entrepreneurial environment with a high degree of individual responsibility.