Skip to main content
Tallo logoTallo logo

Find Jobs

Find Jobs Near You – Available Work in Your Location

Skip to job details

Back to Results

Apply for this opportunity

To apply for this job, you'll continue to an external website or email application.

Photon

GenAI/Python Developer - AI/ML - Newyork, NY - Citi

Review key factors to help you decide if the role fits your goals.
Pay Growth
?
out of 5
Not enough data
Not enough info to score pay or growth
Job Security
?
out of 5
Not enough data
Calculating job security score...
Total Score
81
out of 100
Average of individual scores

Were these scores useful?

Job Description

The Role Design, build, and deploy production-grade Generative AI applications and multi-agent solutions. Develop RAG pipelines, integrate commercial and open-source LLMs, fine-tune models, and build scalable Python APIs. Optimize applications for performance, latency, cost, reliability, and scalability. Implement monitoring, containerization, CI/CD, and cloud deployments while collaborating with engineering, architecture, AI/ML, and product teams. Summary Generated by
Built In Job Description:
GenAI/Python Developer - AI/ML Role Overview We are looking for an experienced GenAI/Python Developer to design, build, and deploy production-grade Generative AI applications and intelligent agent-based solutions. The ideal candidate will have strong expertise in Python, LangGraph, LangChain, LlamaIndex, RAG, vector databases, LLM integration, and multi-agent architectures . The role requires hands-on experience building scalable AI solutions, integrating commercial and open-source LLMs, fine-tuning models, and optimizing GenAI applications for performance, latency, cost, reliability, and production scalability . Key Responsibilities Design, develop, and orchestrate sophisticated Generative AI workflows using LangGraph, LangChain, and multi-agent architectures . Build and maintain advanced Retrieval-Augmented Generation (RAG) solutions using LlamaIndex and vector databases. Design retrieval pipelines focused on improving the accuracy and relevance of LLM-generated responses . Integrate and evaluate multiple commercial and open-source LLMs , including OpenAI, Anthropic, Llama, and Mistral, based on application, performance, and cost requirements. Develop scalable and high-performance backend services and APIs using Python, FastAPI, and asynchronous programming . Fine-tune and customize LLMs for domain-specific use cases using PyTorch, TensorFlow, Hugging Face, LoRA, and QLoRA . Apply advanced prompt engineering techniques to improve LLM response quality and application performance. Optimize GenAI workflows for latency, cost, scalability, and reliability . Implement monitoring and observability for LLM applications using tools such as LangSmith and Langfuse . Containerize AI applications and services using Docker and support production deployments through CI/CD pipelines. Deploy and maintain GenAI solutions across cloud environments such as AWS, Azure, or GCP . Collaborate with engineering, AI/ML, architecture, and product teams to convert business requirements into scalable AI solutions. Troubleshoot and optimize production AI applications, APIs, retrieval pipelines, and model integrations.
Compensation, Benefits and Duration Minimum Compensation:
155,000
USD Maximum Compensation:
217,000 USD Compensation is based on actual experience and qualifications of the candidate. The above is a reasonable and a good faith estimate for the role. Medical, vision, and dental benefits, 401k retirement plan, variable pay/incentives, paid time off, and paid holidays are available for full-time employees. This position is not available for independent contractors No applications will be considered if received more than 120 days after the date of this post. Read Full Description Skills Required Strong Python expertise Hands-on experience with LangGraph, LangChain, LlamaIndex, RAG, vector databases, LLM integration, and multi-agent architectures Experience building scalable Generative AI solutions and integrating commercial and open-source LLMs Experience fine-tuning and customizing LLMs using PyTorch, TensorFlow, Hugging Face, LoRA, and QLoRA Experience developing backend services and APIs using FastAPI and asynchronous programming Experience with prompt engineering and GenAI performance optimization Experience with LLM application monitoring and observability tools such as LangSmith and Langfuse Experience with Docker, CI/CD pipelines, and cloud platforms such as AWS, Azure, or GCP Experience troubleshooting and optimizing production AI applications, APIs, retrieval pipelines, and model integrations View all jobs at Photon View Photon Profile Report Job

Benefits

  • Paid Time Off (PTO)
  • 401(k) Plans
  • Other Retirement and Savings
  • Health Insurance