Core Skill Set:
- Python (advanced, production-grade coding)
- Generative AI (LLMs, prompt engineering, fine-tuning, RAG)
- Agent development using frameworks such as ADK, LangGraph, LangChain
Additional Relevant Skills:
- Experience building end-to-end AI applications (design → deploy → scale)
- Retrieval-Augmented Generation (RAG) pipelines and vector databases (e.g., Pinecone, FAISS)
- API development and integration (FastAPI, REST, microservices)
- Cloud platforms: AWS / Azure / GCP (especially AI/ML services)
- Model evaluation, guardrails, and Responsible AI practices
- Experimentation frameworks, prompt/version management, and observability
- Working knowledge of data pipelines and engineering (ETL, streaming)
- Familiarity with MLOps / LLMOps (CI/CD for models, monitoring, retraining)
- Strong problem-solving with ability to translate business use cases into AI solutions
Compensation, Benefits and Duration
Minimum Compensation: USD 35,000
Maximum Compensation: USD 123,000
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
Skills Required
- Advanced, production-grade Python coding
- Generative AI (LLMs, prompt engineering, fine-tuning, RAG)
- Agent development using ADK, LangGraph, or LangChain
- Experience building end-to-end AI applications (design -> deploy -> scale)
- Retrieval-Augmented Generation (RAG) pipelines and vector databases (Pinecone, FAISS)
- API development and integration (FastAPI, REST, microservices)
- Cloud platforms experience (AWS, Azure, GCP) especially AI/ML services
- Model evaluation, guardrails, and Responsible AI practices
- Experimentation frameworks, prompt/version management, and observability
- Working knowledge of data pipelines and engineering (ETL, streaming)
- Familiarity with MLOps / LLMOps (CI/CD for models, monitoring, retraining)
- Strong problem-solving and ability to translate business use cases into AI solutions
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