The job, in the employer's words
This role is for one of Weekday’s clients
Min Experience: 5+ years
Location: Remote (India)
JobType: full-time
We're looking for a Senior Agentic AI/Generative AI Engineer to help architect our next-generation AI-driven products — from prototyping through production deployment. This is a customer-facing role where you'll move fluidly between solution architecture, hands-on engineering, and client conversations.
Requirements
Key Responsibilities:
- Architect and build scalable Generative AI and agentic AI applications, end to end
- Design LLM-powered workflows and prompt strategies for reflexive, self-learning, multi-agent systems
- Build intelligent AI agents using LangChain and LangGraph for use cases like NL-to-SQL, autonomous task agents, and RAG pipelines
- Select, customize, fine-tune, and optimize state-of-the-art LLMs
- Design and own full ML/GenAI pipelines — training, deployment, monitoring, lifecycle management
- Build APIs, microservices, and integration frameworks to bring AI into enterprise products
- Champion responsible AI practices — mitigating hallucinations, bias, and reliability risks
- Partner directly with customers, product, and engineering to turn business needs into robust AI architecture
- Mentor engineers and help shape our long-term AI platform strategy
Required Qualifications:
- 6+ years in traditional ML, including 2+ years hands-on with Generative AI
- Strong experience with LLMs (GPT and similar), prompt engineering, and agentic systems
- Real-world experience with LangChain/LangGraph or similar agentic frameworks
- Strong Python skills — API wrappers, third-party integrations, internal tooling
- Solid foundation in Transformers, CNNs, RNNs — hands-on with TensorFlow, PyTorch, Scikit-learn
- Experience with NLP, embedding models, and vector databases
- Hands-on work with OpenAI, Llama/Llama2, Azure OpenAI, and other open-source models
- Experience designing distributed, cloud-native architectures (microservices, REST APIs)
- Proficiency with AWS, Azure, or GCP, plus Docker/Kubernetes
- MLOps/LLMOps experience — training, deployment, monitoring, lifecycle management
- Excellent communication skills — you can translate technical depth for non-technical stakeholders
- Bachelor's or Master's in CS, Data Science, Engineering, Math, Statistics, or related field
- Comfort with startup pace and strong ownership mentality
Preferred Qualifications:
- LLM fine-tuning experience (LoRA, RLHF, PEFT)
- Performance optimization (GPU/TPU acceleration, quantization, pruning, distillation)
- AI observability/monitoring tool experience
- Familiarity with AI governance and compliance (GDPR, SOC 2)
- Prior consulting or solution-architecture experience shipping enterprise AI products
- Background in financial services, healthcare, or insurance
Must-have skills
Agentic AI, Generative AI, Artificial Intelligence
Good-to-have skills
Machine Learning, LangChain, LangGraph
Requirements mentioned
- 6+ years in traditional ML, including 2+ years hands-on with Generative AI
- Strong experience with LLMs (GPT and similar), prompt engineering, and agentic systems
- Real-world experience with LangChain/LangGraph or similar agentic frameworks
- Strong Python skills — API wrappers, third-party integrations, internal tooling
- Solid foundation in Transformers, CNNs, RNNs — hands-on with TensorFlow, PyTorch, Scikit-learn
- Experience with NLP, embedding models, and vector databases
- Hands-on work with OpenAI, Llama/Llama2, Azure OpenAI, and other open-source models
- Experience designing distributed, cloud-native architectures (microservices, REST APIs)
- Proficiency with AWS, Azure, or GCP, plus Docker/Kubernetes
- MLOps/LLMOps experience — training, deployment, monitoring, lifecycle management
- Excellent communication skills — you can translate technical depth for non-technical stakeholders
- Bachelor's or Master's in CS, Data Science, Engineering, Math, Statistics, or related field
- Comfort with startup pace and strong ownership mentality
- LLM fine-tuning experience (LoRA, RLHF, PEFT)
- Performance optimization (GPU/TPU acceleration, quantization, pruning, distillation)
- AI observability/monitoring tool experience
- Familiarity with AI governance and compliance (GDPR, SOC 2)
- Prior consulting or solution-architecture experience shipping enterprise AI products
- Background in financial services, healthcare, or insurance
- Must-have skills
- Agentic AI, Generative AI, Artificial Intelligence
- Good-to-have skills
- Machine Learning, LangChain, LangGraph
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