The job, in the employer's words
This role is for one of Weekday’s clients
Salary range: Rs 1000000 - Rs 5000000 (ie INR 10-50 LPA)
Min Experience: 4+ years
Location: Remote India
JobType: full-time
This is a full-time Remote role for a Generative AI Engineer and ML expert.
You can work from home (anywhere in INDIA, job location is in India) and will be responsible for researching and developing our platform with cutting-edge AI agents.
Requirements
Qualifications
- Experience in AI/GenAI solution for prompt engineering, architecture, consulting, and enterprise architecture
- Experience in software development
- Excellent communication skills (customer facing position)
- A deep understanding of GenAI/ML technologies and their implementations
- Experience in the financial, healthcare, or insurance industries is a plus
- Bachelor's/Master's degree in Computer Science/data science or related field
- Demonstrated expertise in statistical analysis and machine learning concepts, with proficiency in Python
- Solid understanding and experience in designing, implementing, and optimizing end-to-end machine learning pipelines.
Minimum 5+ years of experience in Generative AI and Minimum 2+ years in traditional Machine Learning, with a focus on the creation, training, and deployment of services such as recommendation engines, deep learning, and generative AI models.
1. LLM (GPT); prompt engineering for reflex, model and self-learning agents
2. Facility with Python to code wrappers, interface with APIs, develop utilities
3. Experience with packages such as LangChain and LangGraph
4. Experience with assembling intelligent AI agents to implement variety of use cases
5. A recent use case for Agentic AI powered by LLMs: NLQ to SQL translator
6. You MUST have the ability to think through clearly for a solution design,
Experienced in Large Language Models, Transformers, CNN, TensorFlow, Scikit-learn, Pytorch, NLP libraries, Embedding Models, Vector Databases
Hands-on experience with OpenAI, Llama/Llama2 and other open-source models, and Azure OpenAI models Education- Engineering, Math and Statistics foundation, (Data Science/Computer Science preferred)
Requirements added by the job poster
• Bachelor's Degree from IITs/ NITs/ BITs/ IIITs would be preferred
Requirements mentioned
- Qualifications
- Experience in AI/GenAI solution for prompt engineering, architecture, consulting, and enterprise architecture
- Experience in software development
- Excellent communication skills (customer facing position)
- A deep understanding of GenAI/ML technologies and their implementations
- Experience in the financial, healthcare, or insurance industries is a plus
- Bachelor's/Master's degree in Computer Science/data science or related field
- Demonstrated expertise in statistical analysis and machine learning concepts, with proficiency in Python
- Solid understanding and experience in designing, implementing, and optimizing end-to-end machine learning pipelines.
- Minimum 5+ years of experience in Generative AI and Minimum 2+ years in traditional Machine Learning, with a focus on the creation, training, and deployment of services such as recommendation engines, deep learning, and generative AI models.
- 1. LLM (GPT); prompt engineering for reflex, model and self-learning agents
- 2. Facility with Python to code wrappers, interface with APIs, develop utilities
- 3. Experience with packages such as LangChain and LangGraph
- 4. Experience with assembling intelligent AI agents to implement variety of use cases
- 5. A recent use case for Agentic AI powered by LLMs: NLQ to SQL translator
- 6. You MUST have the ability to think through clearly for a solution design,
- Experienced in Large Language Models, Transformers, CNN, TensorFlow, Scikit-learn, Pytorch, NLP libraries, Embedding Models, Vector Databases
- Hands-on experience with OpenAI, Llama/Llama2 and other open-source models, and Azure OpenAI models Education- Engineering, Math and Statistics foundation, (Data Science/Computer Science preferred)
- • Bachelor's Degree from IITs/ NITs/ BITs/ IIITs would be preferred
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