Senior Agentic AI Engineer
New
W
WeekdaySoftware Development
Remote (India)Full-TimeSenior
Salary2,500,000 - 4,500,000 INR per year
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Job Details
- Experience
- Min Experience: 5+ years; 6+ years in traditional ML, including 2+ years hands-on with Generative AI
- Required Skills
- AWSDockerPythonPyTorchTensorflowscikit-learnGenerative AILangChain
Requirements
- Have 6+ years of experience in traditional machine learning, including 2+ years of hands-on Generative AI experience.
- Bring strong experience with LLMs such as GPT, prompt engineering, and agentic systems.
- Have real-world experience with LangChain, LangGraph, or similar agentic frameworks.
- Demonstrate strong Python skills, including API wrappers, third-party integrations, and internal tooling.
- Have a foundation in Transformers, CNNs, and RNNs, with hands-on TensorFlow, PyTorch, and Scikit-learn experience.
- Have experience with NLP, embedding models, and vector databases.
- Have hands-on experience with OpenAI, Llama/Llama2, Azure OpenAI, and other open-source models.
- Have experience designing distributed, cloud-native architectures using microservices and REST APIs.
- Be proficient with AWS, Azure, or GCP, plus Docker and Kubernetes.
- Bring MLOps/LLMOps experience across training, deployment, monitoring, and lifecycle management.
- Hold a bachelor's or master's degree in computer science, data science, engineering, math, statistics, or a related field.
Responsibilities
- Architect and build scalable generative AI and agentic AI applications from prototyping through production deployment.
- Design LLM-powered workflows and prompt strategies for reflexive, self-learning, multi-agent systems.
- Build AI agents with LangChain and LangGraph for NL-to-SQL, autonomous task agents, and RAG pipelines.
- Select, customize, fine-tune, and optimize LLMs.
- Design and own ML/GenAI pipelines covering training, deployment, monitoring, and lifecycle management.
- Build APIs, microservices, and integration frameworks to bring AI into enterprise products.
- Apply responsible AI practices to mitigate hallucinations, bias, and reliability risks.
- Partner with customers, product, and engineering teams to translate business needs into AI architecture.
- Mentor engineers and contribute to long-term AI platform strategy.
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