Role:
Python Developer with Gen AI exp
Location: Bangalore
Work Timings: Asia or EMEA shifts. (Hybrid: 3 days a week WFO mandatory)
Exp: 6 to 10 yrs exp
Notice: 0-30 days
(More than 30 days notice don't apply)
Preferred skills:
Programming:
Advanced Python (async programming, OOP, REST APIs, FastAPI, Flask, Pandas, NumPy)
GenAI Frameworks:
LangChain, LlamaIndex, Haystack, Semantic Kernel
Machine Learning and AI:
Snowflake Cortex, TensorFlow, PyTorch
Key Responsibilities:
- Develop scalable Python applications focused on GenAI
- Knowledge of Snowflake Cortex is a big plus.
- Learn and apply techniques like document chunking and embeddings
- Proficient in both SQL (e.g., PostgreSQL, MySQL) and NoSQL (e.g., MongoDB, Cassandra) databases, including schema design and optimization.
- Design, develop, and fine-tune applications built around foundation models (e.g., LLMs, multimodal models) for specific business needs
- Implement and experiment with generative AI techniques, including but not limited to Retrieval-Augmented Generation (RAG) and prompt engineering
- Lead the implementation of intelligent autonomous agents and multi-agent systems capable of complex decision-making, task execution, and interaction with external systems
- Utilize and contribute to agentic AI frameworks (e.g., LangChain, AutoGen, Semantic Kernel, Crew AI) to build robust and scalable AI agents
- Develop and integrate Model Context Protocol (MCP) solutions to standardize how AI applications access and utilize external data sources, tools, and real-time information
- Lead the development of full-stack applications that integrate generative AI models and agentic systems, ensuring seamless user experiences
- Work with front-end technologies (e.g., React, Angular, Vue.js, JavaScript, TypeScript, HTML, CSS) and back-end frameworks (e.g., Python with Flask/Django/FastAPI, Node.js, Go)
- Design and implement scalable RESTful APIs and microservices to expose AI functionalities
- Deploy, manage, and optimize AI/ML workloads and full-stack applications on at least one major cloud platform (AWS, Azure, or GCP)
- Implement LLMOps/MLOps and DevOps best practices for continuous integration, continuous delivery (CI/CD), model deployment, monitoring, and lifecycle management
- Follow coding best practices and contribute to testing and documentation
- Stay abreast of the latest advancements in generative AI, agentic systems, foundation models, and related fields through continuous research and experimentation
- Collaborate effectively with cross-functional teams, including product managers, data scientists, researchers, and other engineers
- Communicate effectively with both technical and non-technical team members, ensuring alignment and understanding across all project phases.