Senior Technical Consultant - Data and AI
IndiaFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years
- Required Skills
- PythonMachine LearningPyTorchTensorflowDeep LearningLLMGenerative AILangChain
Requirements
- 5+ years of experience in data science, machine learning, deep learning, or AI engineering roles with strong production exposure.
- Strong Python programming skills with a focus on scalable, maintainable, and production-ready code.
- Hands-on experience with deep learning frameworks such as PyTorch, TensorFlow, or scikit-learn.
- Solid understanding of transformer architectures, attention mechanisms, and modern generative AI concepts including RAG, fine-tuning, and transfer learning.
- Experience deploying and operating ML or GenAI systems in production, including monitoring, versioning, and lifecycle management.
- Familiarity with LLM providers and platforms such as OpenAI, Anthropic Claude, LLaMA, AWS Bedrock, or Azure OpenAI.
- Experience with agentic workflows or orchestration frameworks (LangChain, LangGraph, or similar).
- Understanding of cloud platforms (AWS/Azure), including deployment, observability, and serverless patterns.
- Strong communication skills with the ability to explain complex technical concepts to both technical and non-technical stakeholders.
Responsibilities
- Own end-to-end delivery of GenAI and agentic AI solutions, including design, development, testing, deployment, and ongoing production support in client environments.
- Translate solution designs into scalable Python-based services and workflows, integrating LLMs and AI capabilities into enterprise systems.
- Build and maintain agentic and multi-step workflows using orchestration frameworks such as LangGraph, LangChain, or similar tools.
- Design robust APIs and tool interfaces for AI agents with proper schemas, versioning, error handling, and observability integration.
- Implement and maintain retrieval-augmented generation (RAG) pipelines and optimize model performance using structured tuning and evaluation methods.
- Establish monitoring, logging, and evaluation frameworks to ensure reliability, quality, and safety of AI systems in production.
- Collaborate with product, engineering, and domain stakeholders to convert requirements into production-ready AI solutions and participate in architecture and design reviews.
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