Jeeva AI is a superhuman Agentic AI that takes the grunt work out of sales, autonomously discovering and enriching high intent leads, crafting hyper personalized outreach, managing smart follow-ups, and handling objections so your reps focus on what they do best: closing deals.

Trusted by 500+ B2B companies to generate over $50 M in pipeline, Jeeva delivers a consistent 24/7 revenue engine and gives you 50 qualified prospects free during your demo.

As a dynamic startup we are backed by Alt Capital (founded by Jack Altman & Sam Altman), Marc Benioff (CEO Salesforce), Gokul (Board Coinbase), Bonfire (investors in ChowNow), Techstars (investors in Uber), Sapphire (investors in LinkedIn), Microsoft with $4.5M ARR in just 9 months after launch, were not just growing were thriving and making a significant impact in the world of artificial intelligence.

Job Description :

Responsibilities & Expectations :

- Proven experience in developing AI agents, generative AI applications, or multi-agent systems.

- Expertise in Large Language Models (LLMs) such as GPT, T5, BERT, etc., and experience with transformer architectures.

- Hands-on experience with Retrieval-Augmented Generation (RAG) and Agentic RAG frameworks.

- Familiarity with agent-based frameworks (e.g., OpenAI Gym, LangChain, Rasa, Ray RLLib, etc.

- Experience in reinforcement learning, supervised learning, and unsupervised learning to enhance agent capabilities.

- Proficiency in NLP techniques and frameworks (e.g., spaCy, Hugging Face Transformers).

- Strong coding skills in Python, with experience using libraries such as TensorFlow, PyTorch, and Hugging Face for AI model development.

- Experience with cloud platforms (AWS, GCP, Azure) for scalable deployment and containerization (e.g., Docker, Kubernetes).

- Ability to innovate and solve complex problems in real-world settings using generative models and intelligent agents.

- Strong communication skills and the ability to work in cross-functional teams, collaborating with data scientists, product managers, and other stakeholders.

- Ability to integrate external data sources, APIs, or databases into AI agent workflows for dynamic decision-making and knowledge retrieval.