Python Developer
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Summary
Senior Python developer (8-10 years) building GenAI/LLM-powered applications: designing RAG pipelines, prompt engineering, agentic use cases, and deploying models to production. Core stack includes Python ML libraries, LangChain/LlamaIndex, vector databases, and MLOps tooling with Kubernetes/OpenShift and CI/CD.
- 8-10 years of relevant experience in Apps Development or systems analysis role
- Core AI/ML Foundations:
- Strong foundational knowledge in GenAI , Machine Learning (ML modeling), Data Science, Statistics, and AI fundamentals, including Natural Language Processing (NLP), Neural Networks, and Large Language Models (LLMs).
- Generative AI & LLM Expertise:
- Extensive hands-on experience with leading LLMs such as Google Gemini, OpenAI models, Anthropic Claude, Mistral, Llama, and various other open-source LLMs.
- Critical: Deep working knowledge and hands-on experience with Retrieval-Augmented Generation (RAG) pipelines, including advanced RAG techniques and their detailed implementation.
- Proven ability to build, tune, and deploy LLM-based applications using platforms like Vertex AI, Hugging Face, etc.
- Expertise in developing robust prompt engineering strategies, prompt tuning, and creating reusable prompt templates.
- Hands-on experience with agentic framework-based use case implementation.
- Working knowledge of Guardrails and methodologies for assessing the performance and safety of GenAI features.
- Strong programming proficiency in Python is a must, including extensive experience with libraries such as Pandas, NumPy, scikit-learn, PyTorch, TensorFlow, Transformers, FastAPI, Seaborn, LangChain, and LlamaIndex.
- Proficiency in integrating generative AI with enterprise applications using APIs, knowledge graphs, and orchestration tools.
- Hands-on experience with various vector databases (e.g., PG Vector, Pinecone, Mongo Atlas, Neo4j) for efficient data storage and retrieval.
- Experience in dealing with large amounts of unstructured data and designing solutions for high-throughput processing.
- Deployment & MLOps:
- Critical: Hands-on experience deploying GenAI-based models to production environments.
- Strong understanding and practical experience with MLOps principles, model evaluation, and establishing robust deployment pipelines.
- Strong expertise in CI/CD principles and tools (e.g., Jenkins, GitLab CI, Azure DevOps, ArgoCD) for automated builds, testing, and deployments.
- Proven experience with container orchestration platforms like OpenShift or Kubernetes for deploying, managing, and scaling containerized applications in a cloud-native environment.
Skills
- Agentic AI
- AI
- Anthropic
- API
- Argo CD
- Azure
- Azure DevOps
- CI/CD
- Claude
- Cloud
- Cloud Native
- Data Science
- DevOps
- FastAPI
- Gemini
- Generative AI
- GitLab
- Hugging Face
- Jenkins
- Kubernetes
- LangChain
- LlamaIndex
- LLM
- Machine Learning
- Mistral
- MLOps
- Model Evaluation
- MongoDB
- Neo4j
- Neural Networks
- NLP
- NumPy
- OpenAI
- OpenShift
- pandas
- Pinecone
- Prompt Engineering
- Python
- PyTorch
- RAG
- scikit-learn
- Seaborn
- Statistics
- TensorFlow
- Transformers
- Vector Databases
- Vertex AI