Senior Back-End Engineer in Gen-AI

[Harry Wilson]

Job Title: Senior Back-End Engineer in Gen-AI (Must be local)

Location: San Jose, CA (Onsite)

Project Duration: 12 months

Interview: Two rounds of Video interview and IN person interview

We are seeking Senior Backend Dev Engineers to join our team, working with a leading FinTech client to develop innovative AI solutions. Our client is building a GenAI Developer Assistant—an end-to-end GenAI-driven SDLC assistant framework. This platform leverages advanced GenAI technologies to accelerate product development and deployment, fostering innovation and delivering services to customers swiftly.

Job Description:

Qualifications, Education, Certifications and/or Other Professional Credentials

• 7+ years in Python, Java or equivalent programming languages
• 4+ years on cloud providers such as Azure/GCP/AWS
• 3+ years of experience building GenAI services & platforms e.g. adapting models, build RAG pipelines
• 3+ years of experience and familiarity with AI/ML Frameworks such as Pytorch/Tensor flow
• At least 3 Realtime Gen AI project implementation in following areas
• Dev Assistance [ Code generator, Test generator etc.]
• RAG pipeline end to end understanding and implementation.
• Graph or any retrieval method is added advantage.
• At least 2 years of experience, where integrating with various LLM models has done successfully.
• Build services using FASTAPI/Flask/Django
• 3+ years’ experience working with a wide variety of backend data systems such as Big Q, snowflake having experience in data extraction experience in
• streaming platform like kafka, building data pipelines, preparing data sets
• 3+ Years of experience with DevSecOps flows leveraging technologies such as Git, Jenkins, Docker containers, Kubernetes, EKS and AKS, Datadogm Prometheus
• 7+ years of experience in IT, preferably at least 2 years in cloud
• Certifications by cloud providers- GCP is preferred. (certificate in BigQ, VertexAI is an added advantage)
• Experience writing Libraries and Tools

 

Good to Have Tools:

• Testing Frameworks: pytest, JUnit (for testing integrations).
• Version Control: Git (to manage SDLC workflow integration).
• Collaboration Tools: Slack, Jira (for communication and project tracking).
• Multi-Agent Frameworks: Experience with frameworks that support multi-agent coordination and interactions.