

Gen AI Developer on GCP
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Gen AI Developer on GCP, offering a contract length of "unknown" with a pay rate of "unknown," located in Hartford, CT or remote. Requires 2–4 years of GenAI experience on GCP, strong Python skills, and familiarity with LLMs and prompt engineering.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
-
🗓️ - Date discovered
September 30, 2025
🕒 - Project duration
Unknown
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🏝️ - Location type
Remote
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📄 - Contract type
Unknown
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🔒 - Security clearance
Unknown
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📍 - Location detailed
Connecticut, United States
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🧠 - Skills detailed
#A/B Testing #Python #ML (Machine Learning) #AI (Artificial Intelligence) #Scala #TensorFlow #"ETL (Extract #Transform #Load)" #BigQuery #Transformers #PyTorch #Data Science #GCP (Google Cloud Platform) #API (Application Programming Interface) #Cloud
Role description
Job Title: GenAI developer on GCP
Location: Hartford, CT (Preferred), Remote works
==================================== JD ===============
Key Responsibilities
• Design, develop, and deploy Generative AI solutions leveraging GCP Vertex AI, , Gemini, and related services.
• Collaborate with data scientists, MLOps engineers, and product teams to translate business problems into GenAI solutions.
• Fine-tune and deploy foundation models (LLMs, diffusion models, etc.) for use cases like text generation, summarization, code generation, and multimodal applications.
• Implement prompt engineering strategies to optimize GenAI outputs for different business needs.
• Develop scalable APIs and pipelines to serve GenAI models in production.
• Optimize cost, latency, and performance of GenAI workloads on GCP.
• Conduct experiments and A/B testing to evaluate and validate model performance and business value.
• Stay up-to-date with advancements in the GenAI ecosystem and suggest innovative solutions and tools.
Required Qualifications
• 2–4 years of hands-on experience in implementing GenAI projects on GCP.
• Strong knowledge of GCP AI/ML ecosystem, especially:
• Vertex AI
• PaLM API / Gemini
• BigQuery ML
• Cloud Functions / Cloud Run
• GCS, Pub/Sub, Cloud Composer (nice to have)
• Solid experience with LLMs, transformers, and diffusion models.
• Proficiency in Python and frameworks such as TensorFlow, PyTorch, or HuggingFace Transformers.
• Experience with prompt engineering, fine-tuning, and embedding models.
• Familiarity with Vector DBs (e.g., Pinecone, FAISS, Weaviate), and RAG (Retrieval-Augmented Generation) techniques.
• Experience deploying GenAI workloads in production environments
Job Title: GenAI developer on GCP
Location: Hartford, CT (Preferred), Remote works
==================================== JD ===============
Key Responsibilities
• Design, develop, and deploy Generative AI solutions leveraging GCP Vertex AI, , Gemini, and related services.
• Collaborate with data scientists, MLOps engineers, and product teams to translate business problems into GenAI solutions.
• Fine-tune and deploy foundation models (LLMs, diffusion models, etc.) for use cases like text generation, summarization, code generation, and multimodal applications.
• Implement prompt engineering strategies to optimize GenAI outputs for different business needs.
• Develop scalable APIs and pipelines to serve GenAI models in production.
• Optimize cost, latency, and performance of GenAI workloads on GCP.
• Conduct experiments and A/B testing to evaluate and validate model performance and business value.
• Stay up-to-date with advancements in the GenAI ecosystem and suggest innovative solutions and tools.
Required Qualifications
• 2–4 years of hands-on experience in implementing GenAI projects on GCP.
• Strong knowledge of GCP AI/ML ecosystem, especially:
• Vertex AI
• PaLM API / Gemini
• BigQuery ML
• Cloud Functions / Cloud Run
• GCS, Pub/Sub, Cloud Composer (nice to have)
• Solid experience with LLMs, transformers, and diffusion models.
• Proficiency in Python and frameworks such as TensorFlow, PyTorch, or HuggingFace Transformers.
• Experience with prompt engineering, fine-tuning, and embedding models.
• Familiarity with Vector DBs (e.g., Pinecone, FAISS, Weaviate), and RAG (Retrieval-Augmented Generation) techniques.
• Experience deploying GenAI workloads in production environments