Senior GenAI Engineer
New
J
JobgetherArtificial Intelligence
Based in IndiaFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 6+ years of experience in traditional machine learning, including at least 2 years of hands-on Generative AI experience.
- Required Skills
- PythonKubernetesPyTorchTensorflowMLOpsGenerative AILangChain
Requirements
- 6+ years of experience in traditional machine learning.
- At least 2 years of hands-on Generative AI experience.
- Practical knowledge of LLMs, GPT-based models, prompt engineering, and agentic AI systems.
- Real-world experience with LangChain, LangGraph, or similar frameworks.
- Strong Python development skills including API wrappers, integrations, and automation.
- Solid understanding of Transformers, CNNs, and RNNs with experience in TensorFlow, PyTorch, and Scikit-learn.
- Experience with NLP, embedding models, vector databases, and RAG.
- Experience with platforms such as OpenAI, Llama, and Azure OpenAI.
- Proficiency designing distributed, cloud-native architectures using microservices and REST APIs.
- Hands-on experience with major cloud platforms (AWS, Azure, or GCP), Docker, and Kubernetes.
- Practical experience with MLOps and LLMOps.
- Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or related discipline.
Responsibilities
- Architect and build scalable Generative AI and agentic AI applications from initial concepts through production deployment.
- Design LLM-powered workflows, prompt strategies, reflexive systems, and multi-agent solutions.
- Develop intelligent AI agents using LangChain, LangGraph, or comparable frameworks for autonomous task execution and RAG pipelines.
- Evaluate, select, customize, fine-tune, and optimize state-of-the-art large language models.
- Design and own end-to-end ML and GenAI pipelines, covering training, deployment, monitoring, and lifecycle management.
- Build APIs, microservices, and integration frameworks to embed AI capabilities into enterprise products.
- Establish responsible AI practices to mitigate hallucinations, bias, and security risks.
- Collaborate directly with customers and stakeholders to translate business requirements into robust AI solutions.
- Design distributed, cloud-native systems supporting scalable enterprise AI workloads.
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