Data Engineer (AI/ML | Python | GCP ) Experience Level: 5+ years - WFH

🔥 Senior Data Engineer (AI/ML | Python | GCP ) - WFHTHIS IS FULLY REMOTE WORKING OPPORTUNITY.

WE NEED IMMEDIATE JOINERS or someone who can join in less than 1 month.If you are interested and fulfill the below mentioned criteria then please share below information.1. EMAIL ID2. YEARS OF RELEVANT EXPERIENCE.3. UPDATED RESUME.4. CCTC/ECTC5. Notice period

*Key Responsibilities

* 1.

Design & Develop Scalable Data Pipelines

• Build high-throughput, low-latency pipelines to process massive datasets efficiently.
• Ensure pipelines can handle 5–10 TB of structured and unstructured data at scale.
  

2. Data Filtering & Curation

• Optimize data ingestion and transformation for downstream models.
• Implement intelligent scanning mechanisms to filter out relevant data for AI/ML models
  

3. AI/ML Integration

• Leverage GenAI models (e.g., Gemini Pro, OpenAI, GPT) under the Vertex AI umbrella.
• Build and fine-tune custom models when LLMs are cost-prohibitive or less effective.
• Design multi-layered model pipelines balancing accuracy, cost, and performance.
  

4. Cloud Infrastructure

• Architect scalable solutions primarily on GCP (Vertex AI, BigQuery, GKE, Cloud run, PostgreSQL, Dataflow, Pub/Sub).
• Azure knowledge is a plus but not mandatory.
• Experienced in building robust monitoring system.
  

5. Performance Optimization

• Ensure real-time, low-latency processing for high-volume workloads.
• Optimize GPU/CPU-intensive environments to manage cost and efficiency.
  

6. Collaboration & Ownership

• Work closely with data scientists, ML engineers, and solution architects.
• Take end-to-end ownership of assigned tasks and deliverables, working independently where needed.
• Collaborate with global teams and manage 3–4 hours of timezone overlap when required.
  

*Required Skills & Qualifications

* 1.

Programming

* Expert-level Python (mandatory).

2. Data Engineering

:* Hands-on experience with streaming pipelines, and large-scale data ingestion frameworks.

3. AI/ML Expertise

:* Experience integrating and fine-tuning LLMs and GenAI models.* Comfortable working with custom model development where LLMs cannot be applied.* Familiarity with OpenAI, Gemini, Vertex AI, or equivalent platforms.

4. Cloud

:* GCP (mandatory) — experience with Vertex AI, BigQuery, GKE, Cloud Run, PostgreSQL, Dataflow, Pub/Sub, etc.* Azure experience is a plus.

5. Scalability & Low Latency

:* Proven ability to design and implement high-performance, distributed data solutions.

6. High-Volume Data Processing

:* Experience processing massive datasets efficiently within strict timeframes.

*Good to Have

*

• Experience with MLOps and deploying models at scale.
• Knowledge of data governance and compliance when working with sensitive data.
• Hands-on exposure to event-driven architectures and microservices.